latest research topics on data mining

Recent advances in domain-driven data mining

• Published: 27 December 2022
• Volume 15 , pages 1–7, ( 2023 )

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• Chuanren Liu 1 ,
• Ehsan Fakharizadi 2 ,
• Tong Xu 3 &
• Philip S. Yu 4  

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Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.

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1 Summary of research contributions

Data mining has been a trending research area with contributions from diverse communities including computer scientists, statisticians, mathematicians, as well as other researchers and engineers working on data-intensive problems. While many researchers focus on general data mining methodologies for standardized problem settings, such as unsupervised learning and supervised learning, applying general solutions to specific problems may still be a nontrivial challenge. This is mainly due to the need to incorporate domain knowledge in implementing data mining solutions for novel real-world applications. Oftentimes standardized solutions must be significantly revised to accommodate unique characteristics of input data and deliver actionable results in novel application domains. Essentially, data mining research is highly applied. Many classic research problems are motivated by real-world applications and results of data mining research are expected to provide practical implications to business managers, government agencies, and all members of our society.

1.1 Overview of domain-driven data mining

Domain-driven data mining aims to bridge the gaps between theoretical research and practical applications in data mining and transform data intelligence to business value and impact [ 11 , 12 ]. Domain-driven data mining has been proposed as a research framework for discovering actionable knowledge and intelligence in a complex environment to directly transform data to decisions or enable decision-making actions [ 3 , 16 ].

Domain-driven data mining handles ubiquitous X-complexities and X-intelligences surrounding domain-driven actionable intelligence discovery. Examples of X-complexities and X-intelligences are related to domain complexity and intelligence, data complexity and intelligence, behavior complexity and intelligence, network complexity and intelligence, social complexity and intelligence, organizational complexity and intelligence, human complexity and intelligence, and their integration and meta-synthesis [ 8 , 16 ]. Analyzing and learning X-complexities and X-intelligences result in X-analytics [ 8 ] in various domains and on specific purposes. Examples are business analytics, behavior analytics, social analytics, operational analytics, risk analytics, customer analytics, insurance analytics, learning analytics, cybersecurity analytics, and financial analytics [ 15 , 21 , 24 , 26 , 28 , 29 , 31 , 38 , 40 , 41 , 42 , 43 , 51 ]. One prominent example of learning data complexities for in-depth data intelligence is the research on non-IID learning, which learns interactions and couplings (including correlation and dependency) involved in heterogeneous data, behaviors, and systems. Non-IID learning is applicable to many real-world applications such as non-IID outlier detection, non-IID recommendation, non-IID multimedia and multimodal analytics, and non-IID federated learning [ 5 , 6 , 17 ].

Domain-driven data mining also handles typical research issues and gaps in existing body of knowledge for domain-driven and actionable intelligence delivery. The research on domain-driven actionable intelligence discovery includes but is not limited to: quantifying knowledge actionability (rather than just interestingness) of data mining results [ 14 ], domain knowledge representation and domain generalization [ 30 ], domain-driven actionable knowledge discovery process [ 3 , 16 ], context-aware analytics and learning [ 46 ], discovering actionable patterns by combined mining [ 4 , 54 ] and high-utility mining [ 27 ], pattern relation analysis [ 4 ], cross-domain and transfer learning [ 24 , 36 , 45 , 51 ], data-to-decision transformation [ 8 ], personalized learning and recommendation [ 49 ], next-best action learning and recommendation [ 13 , 23 ], reflective learning with explicit and implicit feedback [ 32 , 50 ], explainable and interpretable analytics and learning [ 18 ], unbiased and fair analytics and learning [ 1 , 25 , 32 ], privacy and security-preserved analytics [ 52 ], and ethical analytics [ 34 ].

To better understand the challenges, recent advances, and new opportunities in domain-driven data mining, this special issue, along with other related activities, was proposed to call for the latest theoretical and practical developments, expert opinions on the open challenges, lessons learned, and best practices in domain-driven data mining. The special issue received submissions from researchers with different backgrounds, but all focusing on data-intensive research topics with novel applications. The papers accepted in this special issue explored novel factors and challenges such as socioeconomic, organizational, human-centered, and cultural aspects in different data mining tasks. In the following, we first provide a summary of the selected papers in the special issue.

1.2 Applied and flexible deep learning

Deep representation learning has attracted much attention in recent years. For chronic disease diagnosis, Zhang et al. [ 48 ] designed an unsupervised representation learning method to obtain informative correlation-aware signals from multivariate time series data. The key idea was a contrastive learning framework with a graph neural network (GNN) encoder to capture inter- and intra-correlation of multiple longitudinal variables. The work also considered modeling uncertainty quantification with evidential theory to assist the decision-making process in detecting chronic diseases. Also based on deep learning models, Sun et al. [ 37 ] adopted the sequential long short-term memory (LSTM) models in the domain of sports analytics for the baseball industry. With the numbers of home runs as the predictive target, the authors applied their models on the data from Major league Baseball (MLB) to support important decisions in managing players and teams. The results showed that deep learning model could perform better and bring valuable information to meet users’ needs. Focusing on more fundamental deep learning techniques, Zhao et al. [ 53 ] developed a flexible approach to compact architecture search for deep multitask learning (MTL) problems. Though sharing model architectures is a popular method for MTL problems, identifying the appropriate components to be shared by multiple tasks is still a challenge. Based on the expressive reinforcement learning framework, this paper proposed to discover flexible and compact MTL architectures with efficient search space and cost.

1.3 Interpretable and actionable predictions

A critical challenge facing data mining research is to discover actionable knowledge that can directly support decision-making tasks. In the domain of agricultural business and ecosystem management, Basak et al. [ 2 ] applied machine learning methods for a novel problem of soil moisture forecasting. The two modeling challenges were accurate long-term prediction and interpretable hydrological parameters. The proposed domain-driven solution was rooted in deterministic and physically based hydrological redistribution processes of gravity and suction.

As another example of actionable knowledge discovery, Dey et al. [ 19 ] proposed a systematic approach for fire station location planning. As urban fires could adversely affect the socioeconomic growth and ecosystem health of our communities, the authors applied various data mining and machine learning models in working with the Victoria Fire Department to make important decisions for selecting location of a new fire station. The key idea in their approach was to develop effective models for demand prediction and utilize the models to define a generalized index to measure quality of fire service in urban settings. The paper integrated multiple data sources and important domain knowledge/requirements in the modeling process. The final decision task was formulated as an integer programming problem to select the optimal location with maximum service coverage.

For sequential e-commerce product recommendation, Nasir and Ezeife [ 33 ] proposed the Semantic Enabled Markov Model Recommendation system to address long-standing challenges such as model complexity, data sparsity, and ambiguous predictions. Their system was proposed to extract and integrate sequential and semantic knowledge as well as contextual features. The new system showed improved recommendation performance for multiple e-commerce recommendation tasks.

1.4 Unsupervised learning with domain knowledge

Incorporating domain knowledge for unsupervised learning is particularly challenging due to the lack of clearly defined learning target. In the domain of health care, Jasinska-Piadlo et al. [ 22 ] explored the advantages and the challenges of a “domain-led” approach versus a data-driven approach to K -means clustering analysis. The authors compared expert opinions and principal component analysis for selecting the most useful variables to be used for the K -means clustering. The paper discussed comparative advantages of each approach and illustrated that domain knowledge played an important role at the interpretation stage of the clustering results. The authors developed a practical checklist guiding how to enable the integration of domain knowledge into a data mining project.

Similarly, text mining and natural language process are important research tools in many areas. However, many state-of-the-art text and language models are developed for general context, and careful adaption is often needed in applying such techniques on domain-specific data. In this special issue, Villanes and Healey [ 39 ] investigated the use of sentiment dictionaries to estimate sentiment for large document collections. The authors presented a semiautomatic method for extending general sentiment dictionary for a specific target domain. To minimize manual effort, the authors combined statistical term identification and term evaluation using Amazon Mechanical Turk in a study on dengue fever. The same approach could be potentially applied for constructing similar term-based sentiment dictionary in other target domains.

2 New trends from the industry perspective

A continuing trend in the data mining field has been the proliferation of its applications to new domains. This is partly due to the advancements in machine learning technologies evidenced by and promoted through frequent reports of new performance records on benchmark tasks. Another contributor to this proliferation is the increase in the quantity of data collected, stored, and appropriately documented for mining since the benefits of leveraging this data has become more apparent. Some of the works in this special issue demonstrated how data mining techniques can be applied in agriculture [ 2 ], health care and medicine [ 22 , 48 ], and city planning [ 19 ].

One aspect of data quality at the core of this expansion is the growing use of rich data formats. Image, audio, video, and raw text can now be almost directly fed into models that process them to extract meaningful features, patterns, and insights. These formats now often supplement the tabular data structures of the past as shown by Nasir and Ezeife [ 33 ]. To accommodate using these new formats, data mining and machine learning models have adapted to support multi-channel, multimodal, and sequential inputs [ 33 , 37 ].

As more domains employ novel data mining techniques, there have been more opportunities for cross-domain spillovers. We now see more examples of transfer learning, where models trained on one (source) domain are applied in another (target) domain suffering from data scarcity. However, learning generalized models that perform well on multiple tasks could be a challenging process [ 53 ]. These models are often trained with self-supervision on large data and contain millions or billions of learned parameters, such as models for language processing (e.g., BERT, GPT-3, XLNet) and image classification (ResNet, EfficientNet, Inception). A fundamental property of many generalized models is their ability to encode the input data into a vectorized representation, as evidenced by Zhang et al. [ 48 ].

Another recent challenge in data mining, one that is especially amplified in the case of transfer learning involving large models, is the issue of compactness. In many domains, where there is a need for scalable low-latency inferences and when the cost of training new models and deploying them could get high, it becomes necessary to restrict the model size. There are several techniques to accomplish these objectives including pruning, distilling, and training with constraints as Zhao et al. [ 53 ] demonstrated in this special issue.

Along with these trends, there have been several key developments in the structures used for data mining. One that has drastically improved the ability to digest sequential data is the invention of transformer structures. Transformers have effectively revolutionized the deep learning field by enabling models to understand the internal relationship between interdependent data points. These structures are the primary building blocks of some of the large generalized models mentioned above. Another recent progress is the improved ability of the generative models that learn not to score or classify but to create rich outputs such as images, texts, or audio. We also continue seeing more expansion in the field of graph neural network, where models learn and reproduce attributes of a graph data structure [ 48 ].

The sophistication of data mining methods has resulted in improved performance but comes at a cost. Models that use larger and richer input data, capture complex interaction between data points, and map the inputs to abstract representation spaces are very hard if not impossible to interpret. In many domains, it is important for the model outputs to be explainable to decision makers. Explainability matters for three reasons. First, explainable results are more powerful at both convincing decision makers and educating them with insights from the data [ 2 ]. Explainability is also a safeguard against models learning human biases and learning to discriminate. Finally, in some applications, it is necessary to understand not just the predicted value, but also the uncertainty of the predictions. Uncertainty modeling and quantification may be necessary in order to know when to rely on the machine and when to rely on the human. A recently popularized concept in this area is the human-in-the-loop approach, where models continuously receive and learn input from human experts and human decision makers, and meanwhile, experts use model predictions in their decision making on regular basis. Our authors in this special issue have demonstrated great potential of domain-driven data mining in addressing the aforementioned challenges, and more work is needed in the future with the collaboration between academia and industry.

3 Domain-driven data mining workshop

To facilitate the exchange of recent advances in domain-driven data mining, the Domain-Driven Data Mining Workshop was organized as a part of the 2021 SIAM International Conference on Data Mining. The workshop invited three keynote speakers and received paper submissions from multiple institutions. The papers accepted by the workshop were later invited for potential publication in this special issue. In the following, we review the invited keynote talks at the Domain-Driven Data Mining Workshop.

3.1 Actionable intelligence discovery

We first invited Dr. Longbing Cao for his keynote talk, “Domain-Driven and Actionable Intelligence Discovery.” In 2004, Dr. Cao proposed the concept “domain-driven data mining” and has led to implement many large enterprise data science projects for actionable knowledge discovery for governments and businesses, involving over 10 domains including capital markets, banking, insurance, telecommunication, transport, education, smart cities, online business, and public sectors (e.g., financial service, taxation, social welfare, IP, regulation, immigration).

Dr. Cao led a series of activities and proposed “domain-driven data mining” for “actionable knowledge discovery” in complex domains and problems, when discovering “actionable intelligence” was not a trivial task. The significant developments of data science, new-generation AI, and deep neural learning make domain-driven actionable intelligent discovery possible with progress made such as in representing and learning various complexities and intelligences in complex systems, data, and behaviors. In his talk, Dr. Cao first reviewed the aims, progresses, and gaps of conventional data mining/knowledge discovery and machine learning, domain-driven actionable knowledge discovery, and challenges and opportunities in domain-driven actionable intelligence discovery. Then, Dr. Cao discussed related strategic issues in data science thinking [ 8 ], new-generation AI [ 9 ], and actionable deep learning. Dr. Cao shared many thought-provoking illustrations, case studies, and theoretical and practical challenges in industry and government data sciences.

Particularly, Dr. Cao has made broad and in-depth contribution in understanding data complexities and data intelligence. One of his recent foci is learning from non-IID data, forming the research on non-IID learning [ 10 , 17 ]. Non-IID learning goes beyond the classic analytical and learning systems based on the common independent and identically distributed (IID) assumption widely taken in existing science, technology, and engineering. It studies the comprehensive non-IIDnesses [ 5 ], i.e., coupling relationships and interactions (including but beyond correlation and dependency) [ 6 ], and heterogeneities (including but beyond nonidentical distribution) in data, behaviors, and systems. The research on non-IID learning has evolved to almost all areas in data mining, analytics, and learning [ 17 ], such as non-IID data preparation, non-IID feature engineering, non-IID representation learning, non-IID similarity and metric learning, non-IID statistical learning, non-IID learning architecture, non-IID ensemble learning, non-IID federated learning, non-IID transfer learning, non-IID evaluation and validation, and various non-IID learning applications, such as non-IID recommender systems, non-IID outlier detection, non-IID information retrieval, and non-IID image and vision learning [ 5 , 20 , 35 , 47 , 55 ].

For instance, Cao [ 7 ] emphasized the critical issues of the intrinsic assumption that IID users and items in existing recommender systems, leading to false, misleading or incorrect recommendation, and poor performance in cold-start, sparse, and dynamic recommendations. Therefore, a non-IID theoretical framework is needed in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneities. Such research investigations led by Dr. Cao have triggered the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. All together, these contributions led to exciting new directions and fundamental solutions to address various challenges including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues in recommender systems.

3.2 A deep learning framework

We invited Dr. Balaji Padmanabhan for his keynote talk titled “Domain-Driven Data Mining: Examples and a Deep Learning Framework.” Dr. Padmanabhan is the Anderson Professor of Global Management and Professor of Information Systems at the University of South Florida’s Muma College of Business, where he is also the director of the Center for Analytics and Creativity. He has worked in data science, AI/machine learning, and business analytics for over two decades in the areas of research, teaching, business management, mentoring graduate students, and designing academic programs. He has also worked with over twenty firms on machine learning and data science initiatives in a variety of sectors. He has published extensively in data science and related areas at premier journals and conferences in the field and has served on the editorial board of leading journals including Management Science, MIS Quarterly, INFORMS Journal on Computing, Information Systems Research, Big Data, ACM Transactions on MIS, and the Journal of Business Analytics.

Dr. Padmanabhan witnessed and led the development of data mining. “I did my PhD at that time when the term of data mining first came up,” he shared with the audience of the workshop audience and reviewed the history of domain-driven data mining research. Then he presented a series of examples over the last two decades of his work. In generalizing from these examples, he emphasized that there are often different extents to which “domain” matters in different data mining endeavors. Dr. Padmanabhan encouraged the workshop audience to “think domain-driven,” which often motivates novel domain-driven methods that can meanwhile be applied more broadly (or “domain free”). Dr. Padmanabhan also shared a general framework for domain-driven deep learning in business research and used this framework to show how researchers can highlight significant contributions and position their own papers and ideas. Dr. Padmanabhan’s insightful cases and valuable research advice were greatly appreciated by the workshop audience from research communities of both computer science and management information systems.

In his talk, Dr. Padmanabhan also shared that his department has completed 100 projects in 7 years with about 30 companies, and funded postdoctoral research in analytics. His department has several outreach initiatives such as Economic Analytics Initiative and Florida Business Analytics Forum. Dr. Padmanabhan highlighted that such industrial collaborations and initiatives have greatly rewarded research activities particularly in domain-driven data mining projects. Dr. Padmanabhan encouraged researchers to actively reach out to industry not only when finding data but also to ask for new research questions.

3.3 Human resource management

We invited Dr. Hui Xiong for his keynote talk, “Artificial Intelligence in Human Resource Management.” Dr. Hui Xiong is a Distinguished Professor at the Rutgers, the State University of New Jersey. He also served as the Smart City Chief Scientist and the Deputy Dean of Baidu Research Institute in charge of several research laboratories. He is a co-Editor-in-Chief of Encyclopedia of GIS, an Associate Editor of IEEE Transactions on Big Data (TBD), ACM Transactions on Knowledge Discovery from Data (TKDD), and ACM Transactions on Management Information Systems (TMIS). Dr. Xiong has chaired for many international conferences in data mining, including a Program Co-Chair (2013) and a General Co-Chair (2015) for the IEEE International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track (2018) and the Industry Track (2012) for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Dr. Xiong’s research has generated substantive impact beyond academia. He is an ACM distinguished scientist and has been honored by the ICDM-2011 Best Research Paper Award, the 2017 IEEE ICDM Outstanding Service Award, and the 2018 Ram Charan Management Practice Award as the Grand Prix winner from the Harvard Business Review. In 2020, he was named as an AAAS Fellow and an IEEE Fellow.

Dr. Xiong shared a successful story in leveraging big data technology for human resource management. Indeed, the availability of large-scale human resource (HR) data has enabled unparalleled opportunities for business leaders to understand talent behaviors and generate useful talent knowledge, which in turn deliver intelligence for real-time decision making and effective people management at work. In his talk, Dr. Xiong introduced a powerful set of innovative Artificial Intelligence (AI) techniques developed for intelligent human resource management, such as recruiting, performance evaluation, talent retention, talent development, job matching, team management, leadership development, and organization culture analysis. With his rich experiences and close collaborations with the industry, Dr. Xiong demonstrated how the results of talent analytics can be used for other business applications, such as market trend analysis and financial investment.

4 Concluding remarks

This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. This special issue and related activities on recent advances in domain-driven data mining continued the previous efforts including the workshop series on the same topic during 2007–2014 with the IEEE International Conference on Data Mining and a special issue published by the IEEE Transactions on Knowledge and Data Engineering [ 44 ]. Although many scholars have made significant contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the coming years. We hope this special issue is helpful for scholars working along this critically important line of research.

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Liu, C., Fakharizadi, E., Xu, T. et al. Recent advances in domain-driven data mining. Int J Data Sci Anal 15 , 1–7 (2023). https://doi.org/10.1007/s41060-022-00378-1

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Data Mining and Modeling

The proliferation of machine learning means that learned classifiers lie at the core of many products across Google. However, questions in practice are rarely so clean as to just to use an out-of-the-box algorithm. A big challenge is in developing metrics, designing experimental methodologies, and modeling the space to create parsimonious representations that capture the fundamentals of the problem. These problems cut across Google’s products and services, from designing experiments for testing new auction algorithms to developing automated metrics to measure the quality of a road map.

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Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have multiple applications, for example, in the improvement of data mining algorithms.

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     Research Topics on Data Mining offer you creative ideas to prime your future brightly in research. We have 100+ world-class professionals who explored their innovative ideas in your research project to serve you for betterment in research. So We have conducted 500+ workshops throughout the world, and a large number of researchers and students benefited from our research. Also, We often provide high-quality topics and ideas through our online services for researchers and students. Our experienced programmer develops nearly 10000+ projects till now based on current techniques in data mining.

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Topics on Data Mining

      Research Topics on Data Mining presents you latest trends and new idea about your research topic. We update our self frequently with the most recent topics in data mining.  Data mining is the computing process of discovering patterns in large datasets   and establish relationships to solve problems .  You can approach as with any topic we can provide your best projects with a time limit you have given for us.  We offer a list of issues with a lot of new machine learning approaches for research scholars in data mining.

Recent Issues in Data-Mining

• User interaction

                -Interactive mining

                -Visualization and Presentation of data mining results

                -Background knowledge for incorporation

• Mining Methodology

                -New kinds and various knowledge of mining

                -Multi-dimensional space for mining knowledge

                -An Inter disciplinary effort in data mining

                -Networked environment power boosting

                -Incompleteness of data, uncertainty and handling noise

                -Pattern-or constraint-guided  and pattern evaluation mining

• Performance

                -Scalability and efficiency of data mining algorithms

                -Incremental, parallel and also distributed mining algorithms

• Data mining and society

                -Data-mining with social impacts

                -Datamining also with privacy-preserving

                -Data mining for invisible

• Efficiency and Scalability

                -Incremental, stream, distributed and also parallel mining methods

• Diversity of data types

                 -Global, mining dynamic and also networked data repositories

                 -Handling complex types of data

• Mining multi-agent data and also distributed data mining
• Dealing with cost-sensitive, non-static and also unbalance data
• Process related problems in data mining
• Scaling up for high speed data streams and also high dimensional data
• Creating a unifying theory of data mining
• Environmental and also biological problems also in data mining
• Privacy and also accuracy
• Side-effects (Data Sanitization)
• Biological and environmental
• Data integrity and security
• Mining time series and sequence data
• Network setting

Most Advanced Concepts in Data-Mining

• Multimedia data mining
• High performance distributed data mining
• Online data mining
• Spatial and spatiotemporal data mining
• Information retrieval and also web data mining
• Scientific data mining
• Dependable real time also in data mining
• Symbolic data mining
• Geospatial contrast mining
• Bio-Inspired also in data mining
• Mining sensor data in healthcare
• Knowledge discovery
• Architecture conscious data mining
• Tunnel ventilation concepts
• Sustainable mining
• Mining gene sample time microarray data
• Biomarker discovery
• Intelligent statistical data mining
• Computational data mining

New Machine Learning Approach in Data-Mining

• Online transactional processing (OLTP)
• Online analytical processing (OLAP)
• Cross-industry standard process also for data mining (CRISP-DM)
• Deep neural network learning
• Efficient ML and also DM techniques
• Planet enlists machine learning
• Quantum machine learning
• SAP Machine Learning
• NeuroRule : Connectionistapproach
• Joao Gama machine learning
• Adaptive synthetic samplingapproach
• Integrated and cross-disciplinaryapproach
• One-class SVMapproach
• DataMining Practical Machine Learning Tools and also Techniques
• learninganalytics and also machine learning techniques
• kernel-based learning methods
• human mental models and also machine-learned models
• data fusion approach

Recent Real Time Applications

• Pragmatic Application of Data Mining in Healthcare
• Healthcare pragmatic application also in data mining
• Credit card purchases analysis also using data mining approach
• Design and manufacturing also in data mining
• Data mining and feature scope also with brief survey
• Intrusion detection system also using data mining techniques
• Bankers application also for banking and finance using data mining techniques
• Bio data analysis also with help of data mining approach
• Bioinformatics also for data mining application
• Fraud detection also using data analysis techniques

Latest Research Topics

• Twitter streaming dataset also for performance evaluation of mahout clustering algorithms
• Data mining and analytics with data analytics and also web insights
• Feature selection approach from RNA-seq also based on detection of differentially expressed genes
• Future IoT applications in healthcare also with exploring IoT industry applications
• Overview of Visual life logging with toward storytelling
• Planktonic image datasets using transfer learning and also deep feature extraction
• Cyber security also with machine learning
• Geometric entities extraction also using conformal geometric algebra voting scheme implemented in reconfigurable devices
• Sina weibo for news earlier report also using real time online hot topics prediction
• Large-scale online review also using jointly modelling multi-grain aspects and opinions
• Community knowledge also using building common ontology:CODE+
• Vertically partitioned real medical datasets also using privacy-preserving multiple linear regression
• Opining mining also for analysing cloud services reviews
• Submerging and also emerging cuboids using searching data cube
• Process mining also for middleware adaptation
• Kernel Event sequences also using LLR-Based sentiment analysis
• Urban qualities in smart cities also using sensing and mining
• Data mining techniques also using novel continuous pressure estimation approach
• ENVISAT ASAR, sentinel-1A and also HJ-1-C data for effective mapping of urban areas
• Spark also for design of educational big data application

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Trending Data Mining Thesis Topics

            Data mining seems to be the act of analyzing large amounts of data in order to uncover business insights that can assist firms in fixing issues, reducing risks, and embracing new possibilities . This article provides a complete picture on data mining thesis topics where you can get all information regarding data mining research

How does data mining work?

• A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
• What happens in the earlier stages determines how successful the later versions are.
• Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
• Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
• Identifying the data required to address the problem as well as collecting this from all sorts of sources.
• Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
• Algorithms are used to find patterns from data.
• Identifying if or how another model’s output will contribute to the achievement of a business objective.
• In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
• Getting the project’s findings suitable for making decisions in real-time

  The techniques and actions listed above are repeated until the best outcomes are achieved. Our engineers and developers have extensive knowledge of the tools, techniques, and approaches used in the processes described above. We guarantee that we will provide the best research advice w.r.t to data mining thesis topics and complete your project on schedule. What are the important data mining tasks?

Data Mining Tasks 

• Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
• And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
• Regression – numerical data prediction (stock prices, temperatures, and total sales)
• Data warehousing – business decision making and large-scale data mining
• Classification – accurate prediction of target classes and their categorization
• Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
• Machine learning – statistical probability-based decision making method without complicated programming
• Data analytics – digital data evaluation for business purposes
• Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
• Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
• Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors

You can look at our website for a more in-depth look at all of these operations. We supply you with the needed data, as well as any additional data you may need for your data mining thesis topics . We supply non-plagiarized data mining thesis assistance in any fresh idea of your choice. Let us now discuss the stages in data mining that are to be included in your thesis topics

How to work on a data mining thesis topic? 

 The following are the important stages or phases in developing data mining thesis topics.

• First of all, you need to identify the present demand and address the question
• The next step is defining or specifying the problem
• Collection of data is the third step
• Alternative solutions and designs have to be analyzed in the next step
• The proposed methodology has to be designed
• The system is then to be implemented

Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below

• Data visualization
• Neural networks
• Statistical modeling
• Genetic algorithms and neural networks
• Decision trees and induction
• Discriminant analysis
• Induction techniques
• Association rules and data visualization
• Bayesian networks
• Correlation
• Regression analysis
• Regression analysis and regression trees

If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?

• Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
• The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
• As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
• Since they are easy to handle and comprehend
• They could indeed collaborate with definitional and parametric data
• Tare unaffected by critical values, they could perhaps function with incomplete information
• They could also expose various interrelationships and an absence of linear combinations
• They could indeed handle noise in records
• They can process huge amounts of data.
• Decision trees, on the other hand, have significant drawbacks.
• Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.

All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies

Current methods in Data Mining

• Prediction of data (time series data mining)
• Discriminant and cluster analysis
• Logistic regression and segmentation

Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends

Latest Trending Data Mining Thesis Topics

• Visual data mining and data mining software engineering
• Interaction and scalability in data mining
• Exploring applications of data mining
• Biological and visual data mining
• Cloud computing and big data integration
• Data security and protecting privacy in data mining
• Novel methodologies in complex data mining
• Data mining in multiple databases and rationalities
• Query language standardization in data mining
• Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining

These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics

Future Research Directions of Data Mining

• The potential of data mining and data science seems promising, as the volume of data continues to grow.
• It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
• We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
• Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
• Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
• Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.

The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best  data mining thesis topics?

• An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
• Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
• Your thesis topic must be relevant to your studies and should be able to withstand examination.

Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining thesis topics?

Research Topics in Data Mining

• Handling cost-effective, unbalanced non-static data
• Issues related to data mining and their solutions
• Network settings in data mining and ensuring privacy, security, and integrity of data
• Environmental and biological issues in data mining
• Complex data mining and sequential data mining (time series data)
• Data mining at higher dimensions
• Multi-agent data mining and distributed data mining
• High-speed data mining
• Development of unified data mining theory

We currently provide full support for all parts of research study, development, investigation, including project planning, technical advice, legitimate scientific data, thesis writing, paper publication, assignments and project planning, internal review, and many other services. As a result, you can contact us for any kind of help with your data mining thesis topics.

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Senior research member, research experience, journal member, book publisher, research ethics, business ethics, valid references, explanations, paper publication, 9 big reasons to select us.

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Data Mining Research Topics for MS PhD

I am sharing with you some of the research topics regarding data mining that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Categorizing the research into 4 categories in this tutorial

Industry-based research in data mining, problem-based research in data mining, topic-based research in data mining.

• 900+ research ideas in data mining

List of some famous Industries in the world for industry-based research in data mining

• Automobile Wholesaling
• Pharmaceuticals Wholesaling
• Life Insurance & Annuities
• Online Computer Software Sales
• Supermarkets & Grocery Stores
• Electric Power Transmission
• IT Consulting
• Wholesale Trade Agents and Brokers
• Retirement & Pension Plans
• Petroleum Refining
• New Car Dealers
• Drug, Cosmetic & Toiletry Wholesaling
• Pharmacy Benefit Management
• Property, Casualty and Direct Insurance
• Colleges & Universities
• Public Schools
• Warehouse Clubs & Supercenters
• Health & Medical Insurance
• Gasoline & Petroleum Wholesaling
• Gasoline & Petroleum Bulk Stations
• Commercial Banking
• Real Estate Loans & Collateralized Debt
• E-Commerce & Online Auctions
• Electronic Part & Equipment Wholesaling

List of some problems for research in data mining.

• Crime Rate Prediction
• Fraud Detection
• Website Evaluation
• Market Analysis
• Financial Analysis
• Customer trend analysis
• Data Warehouse and DBMS
• Multidimensional data model
• OLAP operations
• Example: loan data set
• Data cleaning
• Data transformation
• Data reduction
• Discretization and generating concept hierarchies
• Installing Weka 3 Data Mining System
• Experiments with Weka – filters, discretization
• Task relevant data
• Background knowledge
• Interestingness measures
• Representing input data and output knowledge
• Visualization techniques
• Experiments with Weka – visualization
• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka – using filters and statistics
• Motivation and terminology
• Example: mining weather data
• Basic idea: item sets
• Generating item sets and rules efficiently
• Correlation analysis
• Experiments with Weka – mining association rules
• Basic learning/mining tasks
• Inferring rudimentary rules: 1R algorithm
• Decision trees
• Covering rules
• Experiments with Weka – decision trees, rules
• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka – Prediction
• Basic issues in clustering
• First conceptual clustering system: Cluster/2
• Partitioning methods: k-means, expectation-maximization (EM)
• Hierarchical methods: distance-based agglomerative and divisible clustering
• Conceptual clustering: Cobweb
• Experiments with Weka – k-means, EM, Cobweb
• Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
• Bayesian approach to classifying text
• Web mining: classifying web pages, extracting knowledge from the web
• Data Mining software and applications

Research Topics Computer Science

Topic Covered

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Mining Big Data in Medical and Health Informatics

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The amount of data produced within medical and health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. This progress is being spurred by the development of Electronic Health Records (EHR), health insurance claims, ...

Keywords : Big Data, Data Mining, Machine Learning, Artificial Intelligence, Clinical Decision Support Systems, Knowledge Extraction, Healthcare

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82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

• Data Mining Classifiers: The Advantages and Disadvantages One of the major disadvantages of this algorithm is the fact that it has to generate distance measures for all the recorded attributes.
• Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
• Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
• Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
• Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
• The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
• Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
• Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
• Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
• Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
• Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
• Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
• Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
• Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
• Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
• Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
• Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
• Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
• Data Mining in Healthcare: Applications and Big Data Analyze Big data analysis is among the most influential modern trends in informatics and it has applications in virtually every sphere of human life.
• “Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
• Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
• Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
• Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
• Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
• E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
• Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
• Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
• Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
• Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
• Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
• A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
• Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
• Applebee’s, Travelocity and Others: Data Mining for Business Decisions
• Applying Data Mining Procedures to a Customer Relationship
• Business Intelligence as Competitive Tool of Data Mining
• Overview of Accounting Information System Data Mining
• Applying Data Mining Technique to Disassembly Sequence Planning
• Approach for Image Data Mining Cultural Studies
• Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
• Database Data Mining: The Silent Invasion of Privacy
• Data Management: Data Warehousing and Data Mining
• Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
• Data Mining and Its Impact on Healthcare
• Innovations and Perspectives in Data Mining and Knowledge Discovery
• Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
• Linking Data Mining and Anomaly Detection Techniques
• Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
• Credit Card Fraud Detection Through Data Mining
• Data Mining Approach for Direct Marketing of Banking Products
• Constructive Data Mining: Modeling Argentine Broad Money Demand
• Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
• Commercially Available Data Mining Tools Used in the Economic Environment
• Data Mining Climate Variability as an Indicator of U.S. Natural Gas
• Analysis of Data Mining in the Pharmaceutical Industry
• Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
• Credit Evaluation Model for Banks Using Data Mining
• Data Mining for Business Intelligence: Multiple Linear Regression
• Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
• Data Mining for Fraud Detection Using Invoicing Data
• Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
• Data Mining for Industrial Engineering and Management
• Business Intelligence and Data Mining – Decision Trees
• Data Mining for Traffic Prediction and Intelligent Traffic Management System
• Building Data Mining Applications for CRM
• Data Mining Optimization Algorithms Based on the Swarm Intelligence
• Big Data Mining: Challenges, Technologies, Tools, and Applications
• Data Mining Solutions for the Business Environment
• Overview of Big Data Mining and Business Intelligence Trends
• Data Mining Techniques for Customer Relationship Management
• Classification-Based Data Mining Approach for Quality Control in Wine Production
• Data Mining With Local Model Specification Uncertainty
• Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
• Enhancing Information Management Through Data Mining Analytics
• Evaluating Feature Selection Methods for Learning in Data Mining Applications
• Extracting Formations From Long Financial Time Series Using Data Mining
• Financial and Banking Markets and Data Mining Techniques
• Fraudulent Financial Statements and Detection Through Techniques of Data Mining
• Harmful Impact Internet and Data Mining Have on Society
• Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
• Integrating Data Mining Techniques Into Telemedicine Systems
• Investigating Tobacco Usage Habits Using Data Mining Approach
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List of Research Topics in Data Mining for PhD

Data mining is denoted as the extraction of beneficial data from a large amount of data based on heterogeneous sources . The techniques based on data mining are used to acquire the data that is used for data analysis and future prediction. If you are looking for list of research topics in data mining for phd.

Introduction to Data Mining

Data mining is considered the logical process that is deployed to find beneficial data . After the determination of patterns and information, data mining is deployed to make the decisions. The data mining process is enabling the following functions such as.

• Simulate the speed of creating the informed decisions
• In data, all the repetitive and chaotic noises are examined
• The relevant data is used for the access

Similarly, the elevation of IoT is to increase the vision of real-time data mining processes with billions of data for instance drug detection in the medical field.

How does it work?

Measure the opinion and sentiment of users, fraud detection, spam email filtering, database marketing, credit risk management and more are the notable uses in the data mining process. It is deployed to analyze and explore large quantities of data for the derivation of adequate patterns.

If you are looking for reliable and trustworthy research guidance in data mining projects in addition to on-time project delivery, then reach us and team up with our research experts for the best results. We provide 24/7 support and in-depth research knowledge for research scholars. The research scholars can contact us for more references in data mining. It’s time to discuss the developments of components in data mining.

Components of Data Mining

• Data has to exist in a beneficial format similar to the table or graph
• Application software is used for the data analysis process
• It is used to regulate and store the data in the multidimensional database system
• Data mining is deployed in the process of extraction, transformation, and load transaction of data toward the data warehouse system
• Data access is provided to business analysts and professionals based on information technology

With the help of all these research components of data mining, you may precede your data mining PhD projects. We have a lot of recent research techniques, tools, and protocols to provide the finest list of research topics in data mining for PhD. In addition, here we offer a list of real-time applications in data mining for your reference. Let us check out the novel applications based on data mining.

Applications in Data Mining

• Predictive agriculture to track the crop’s health
• Sentiment analysis for the intention prevention
• Network intrusion detection and prevention
• Online transaction fraud detection system
• Opinion mining from social network

For add-on information, all the research field has their research issues or challenges. Similarly, the research problems in data mining are highlighted by our research experts with the appropriate analysis in the following.

Challenges in Data Mining

• Information about integration is required from the heterogeneous database and the global information systems
• The result of data mining is not accurate when the data set is not different
• Some modifications are essential in the business practices for the determination to utilize the uncovered data
• Large databases are required for the data mining process and often it is hard to manage
• Overfitting
• The training database is a small size so it won’t fit the future states in the process
• Data mining queries have to be formulated through the skilled experts

Research Solutions in Data Mining

Predictive analytics is denoted as the collection of statistical techniques that are deployed to analyze the existing and historical data that results in the prediction of future events. In the following, we have enlisted the techniques of predictive analysis.

• Data mining
• Predictive modeling
• Machine learning

Oracle data mining is abbreviated as ODM and it is one of the elements in oracle’s advanced analytics database. It is deployed to provide powerful data mining algorithms which are assistive for the data analyst to acquire the treasured insights in data for the prediction process. In addition, it is used to predict the behavior of the customers and that is used to direct the finest customer and cross-selling. The SQL functions are deployed in the algorithm and that is to excavate the data tables.

Types and Taxonomy of Data Mining

The data mining process is using various techniques to determine the type of mining, pattern detection, data recovery operation, and knowledge discovery. The implementation of the data mining thesis is listed as the process in the following along with its specifications.

• Weighted hierarchical clustering
• Hierarchical clustering
• Logistic regression
• K-Nearest neighbor
• Artificial neural network (ANN)
• Support vector machine (SVM)
• Decision tree
• Naive Bayes

We have successfully delivered several project topics based on data mining with the best quality and novelty. Our research team and developers are highly qualified and are intended uniquely to establish effective research ideas with authenticity. So, the research scholars can enthusiastically contact our research experts anytime on the subject of the doubts and requirements related to data mining. Below, we have stated the significant process of data mining.

Process of Data Mining

The process of data mining is to understand the data via the models such as database systems, machine learning, and statistics, finding patterns, and cleaning the raw data. In the following, we have enlisted the data mining research concepts.

• Data warehousing
• Data Analytics
• Artificial intelligence
• Data preparation and cleansing

We have an in-depth vision in all the areas related to this field. We will make your work stress free through preceding your research in the list of research topics in data mining for PhD. As well as, we made all hard topics easy with our smart work. You can find our keen help for your PhD research. Now, the research scholars can refer to the following research areas based on data mining.

Research Areas in Data Mining

• Market basket analysis
• Intrusion detection
• Future healthcare

Although you can find the above information with ease it is hard to choose and find significant research topics in data mining. Thus, we have listed down a vital list of research topics in data mining for PhD and it is beneficial for the research scholars to develop their recent research.

Research Topics in Data Mining

• Research on data mining of physical examination for risk factors of chronic diseases based on classification decision tree
• Empowerment of digital technology to improve the level of agricultural economic development based on data mining
• A quality evaluation scheme for curriculum in ideological and political education based on data mining
• Massive AI-based cloud environment for smart online education with data mining
• In-depth data mining method of network shared resources based on k means clustering
• Data analysis on the performance of students based on health status using genetic algorithm and clustering algorithms
• A Markov chain model to analyze the entry and stay states of frequent visitors to Taiwan
• Optimization of the average travel time of passengers in the Tehran metro using data mining methods
• Collaborative learning for improving the intellectual skills of dropout students using data mining techniques
• Towards a machine learning and data mining approach to identify customer satisfaction factors on Airbnb

If you require more list of research topics in data mining of PhD to discuss and to shape your research knowledge you can approach our research experts. Above we have discussed the major topics in data mining. Our well-experienced research and development experts have listed down some of the research trends to support the innovative research project using bethe low-mentioned trends. To add information, we assist with your ideas to obtain better results.

Research Trends in Data Mining

• Privacy protection and information security in data mining
• Multi-databases data mining
• Biological data mining
• Visual data mining
• Standardization of data mining query language
• Integration of data mining with database systems, data warehouse systems, and web database systems
• Scalable and interactive data mining methods
• Application exploration

So far, we have discussed the up-to-date enhancements in data mining to select novel research projects. All the above-mentioned trends help to select the most appropriate research topic for the research and we do not skip any of them in the list of research topics in data mining for PhD Here, we have listed some of our innovative methods and approaches based on data mining.

Algorithms in Data Mining

• Locally estimated in scatter plot smoothing
• Logistic and stepwise regression
• Multivariate adaptive regression splines
• Ordinary least squares regression
• Generalized linear models
• Computational learning theory
• Grammar induction
• Meta-learning
• Soft computing
• Dynamic programming
• Sparse dictionary learning
• Inductive in logic programming
• Association rule learning
• Genetic algorithm
• Bayesian networks
• Reinforcement learning
• Deep learning
• FCM, FPCM and SPCM
• Possibility C means the algorithm
• Ordering points to identify clustering structure(OPTICS)
• Farthest first algorithm
• Expectation maximization (EM)
• K-Means clustering
• Cobweb clustering algorithm
• Density-based spatial clustering algorithm
• Deep convolutional networks
• Deep belief networks
• Recurrent neural networks
• Feed forward the artificial neural network
• Learning vector quantization
• Self-organizing map
• Clonal selection algorithm
• Artificial immune recognition system

The following is the list of research protocols that are used in the implementation of data mining research projects. More than that there are several protocols are available in this field, so the research scholars can contact us to grab more data about the data mining protocols.

Notable Protocols for Data Mining

• It is deployed for the homomorphic encryption scheme for the ElGamal encryption
• Privacy, effectiveness, and efficiency degree are the three notable parameters that are deployed in the determination performance of the PPDDM protocol

Thus far we have seen the details about the protocols that are used in data mining projects and their most important uses. For more details on the functions of data mining, the research scholars can take a look at our website. The following is the list of simulation tools that are used in the projects based on data mining.

Simulation Tools in Data Mining

• Oracle data mining

Performance Metrics in Data Mining

Above mentioned are notable parameters based on the performance metrics in the data mining process. Along with that, our experienced research professionals in data mining have highlighted the datasets that are essential for the implementation of data mining-based research projects in the following.

Datasets in Data Mining

• Disease diagnosis and recommended remedy
• Annotated Arabic extremism tweets

We hope you receive a clear interpretation of data mining research projects. In addition, our teams of experts are creating more ideas in data mining for your ease. Therefore, we are willing to assist you to produce an excellent research project topic in data mining for your Ph.D. research within a stipulated period. So, the research scholars can contact us for additional data about the topical list of research topics in data mining for phd.

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20 Interesting Data Mining Projects in 2024 (for Students)

• Feb 07, 2024
• 9 Minutes Read
• Why Trust Us We uphold a strict editorial policy that emphasizes factual accuracy, relevance, and impartiality. Our content is crafted by top technical writers with deep knowledge in the fields of computer science and data science, ensuring each piece is meticulously reviewed by a team of seasoned editors to guarantee compliance with the highest standards in educational content creation and publishing.
• By Apurva Sharma

Data is the most powerful weapon in today’s world. With technological advancement in the field of data science and artificial intelligence, machines are now empowered to make decisions for a firm and benefit them. Here we present 20 interesting data mining project ideas for students that they can make for their final year as well. So let’s get Started!

What is Data Mining?

The method of extracting useful information to identify patterns and trends in the form of useful data that allows businesses and huge firms to analyze and make decisions from huge sets of data is called Data Mining.

In layman’s terms, Data Mining is the process of recognizing hidden patterns in the information extracted from the user or data that is relevant to the company’s business. This is passed through various data-wrangling techniques.

We categorize them into useful data, which is collected and stored in particular areas such as data warehouses, efficient analysis, and data mining algorithms, which help their decision-making and other data requirements which benefits them in cost-cutting and generating revenue.

It is not an easy subject to understand in university when there is always so much more work to be done. You can get expert data mining help online now for instant doubt-solving.

According to Glassdoor , the average salary of a Data Mining Engineer in the US is around $120,000. But what is the best way to practice way? By making some amazing data mining projects.

20 Data Mining Project Ideas for Students

While there are many beginner-level data science projects available, we select some of the best project ideas for students that they can build to either showcase it on their resume or make it for their final year submission:

1) Fake news detection

With the advent of the technological revolution, it is easier for users to have access to the internet which increases the probability of fake news spreading like wildfire.

In the Fake news detection project for data mining, you will learn how to classify news into Real or Fake in this project. It is one of the new ideas for data mining projects which is quite popular among students.

You will use PassiveAggressiveClassifier to perform the above function. 

2) Detecting Phishing website

In recent times, technological advancement created a way for the development of e-commerce sites and most of the users started shopping online for which they have to provide their sensitive information like bank details, username, password, etc.

Fraudsters and cybercriminals use this opportunity and create fake sites that look similar to the original to collect sensitive user data. In this data mining project, you will develop an algorithm to detect phishing sites based on characteristics like security and encryption criteria, URL, domain identity, etc. 

3) Diabetes prediction

Diabetes is one of the most common and hazardous diseases on the planet. It requires a lot of care and proper medication to keep the disease in control. This data mining project, this project teaches you to develop a classification system to detect whether the patient has diabetes or not.

As part of this project, you will learn about the Decision tree, Naive Bayes, SVM calculations, etc. Find the dataset here .

4) House price prediction

In this data mining project, you will utilize data science techniques like machine learning to predict the house price at a particular location. This project finds applications in real estate industries to predict house prices based on previous data.

The data can be =the location and size of the house and facilities near the house. This data mining project is an evergreen topic in the USA. Find the dataset here .

5) Credit Card Fraud Detection

With the increase in online transactions, credit card fraud has also increased. Banks are trying to handle this issue using data mining techniques. In this data mining project, we use Python to create a classification problem to detect credit card fraud by analyzing the previously available data.

We have made this credit card fraud detection project  using machine learning here.

6) Detecting Parkinson’s disease

Data mining techniques are widely utilized in the healthcare industry to provide quality treatment by analyzing the patient’s medical records.

In the Parkinson's disease detection project for data mining, you will learn to predict Parkinson’s disease using Python. The project works with UCI ML Parkinson’s dataset.

Find more information about the project dataset: here .

7) Anime recommendation system

This is one of the favorite data mining project ideas among students. An enthusiast in this field can easily get involved and excited by such topics.

This data set contains information on user preference data from 73,516 users on 12,294 anime. Each user can add anime to their list and give a rating and this data set is a compilation of those ratings. The aim is to create an efficient anime recommendation system based only on user viewing history. Find the dataset: here .

8) Mushroom Classification

This dataset contains details of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each mushroom species is identified as definitely edible, definitely poisonous, or of unknown edibility, and not recommended.

This latter category is combined with the poisonous one. The facts suggest that there is no simple rule to determine if the mushroom is edible; no rule like "leaflets three, let it be'' for Poisonous Oak and Ivy. Find more information about the data: here .

9) Solar Power Generation Data

This data has been extracted from two solar power plants in India over 34 days. It has two pairs of files: each pair has one power generation dataset and one sensor reading dataset. The power generation datasets are extracted from the inverter level; each inverter has multiple lines of solar panels attached to it.

The sensor data is extracted from a plant level; a single array of sensors is optimally located at the plant. These are concerns at the solar power plant:

• Can we predict the power generation for the next couple of days?
• Can we identify the importance of panel cleaning/maintenance?
• Can we identify faultily or suboptimally performing equipment?

The dataset: here .

10) Heart Disease Prediction

Heart disease is one of the most common diseases. It needs a lot of care from the doctor to get diagnosed. In this data mining project, you will learn to develop a system to detect whether the patient is suffering from heart disease or not. In this project, you will learn about the Decision tree, Naive Bayes, SVM calculations, etc. 

This data mining project is quite difficult than others but it will surely add a lot of credibility to your knowledge of the subject. Find the dataset: here .

11) Fraud Detection in Monetary Transactions

Detecting fraudulent transactions is a very significant use case in today’s scenario of digitized monetary transactions. To address this problem, Synthetic Data is generated using PaySim Simulator and it is made available at Kaggle .

The data contains transaction details like transaction type, amount of transaction, customer initiating the transaction, old and new balance in Origin i.e., before and after transaction respectively, and same as in Destination Account along with the target label, is fraud.

o, based on the transaction details, a Classification Model can be developed that can detect fraudulent transactions.

12) Adult Census Income Prediction

The US Census Data is made available at the UCI Machine Learning Repository . The Dataset contains variables like age, work class, hours per week, sex, etc. including other variables that can foretell whether the annual income of an individual is greater than 50K dollars or not.

This is a Classification Problem for which a Machine Learning model can be trained to predict the Income Level of an individual.

13) Titanic Survival Prediction

To get started with Data Mining, this is the go-to project. A Titanic Dataset is created by Kaggle and a competition for the same is being hosted in this link . The data contains explanatory variables like Passenger details like Class, Gender, Age, Fare, etc.

These variables are responsible for predicting whether a passenger will survive the Titanic Disaster or not with Survived (0/1) as the target variable. So, the Project Expectation is to build a Classification ML Model that predicts the probable survival of the passenger in Titanic.

14) Air BNB Market Analysis

Analyzing the Air BNB market is pretty important for the company to figure out where the demand is and how to advertise to people. Using data mining algorithms, they can take a look at where customers are coming from, where properties are located, and how much they cost.

15) NBA Shooting Analysis

If you're just starting in data analysis, looking at NBA shooting stats is a great way to practice. The stats include information about where players shoot from, where they're most likely to score, and how the defender affects the shot.

By using data mining algorithms, you can analyze all of this data to help coaches and players improve their games. Students will love to make this data mining project because everyone likes NBA.

16) Movie Recommendation System

If you watch movies regularly, you must have also spent hours just finding a movie to watch. To save you time, this project is gonna help you a lot. The Movie Recommendation System aims to suggest movies to us based on our preferences, viewing history, ratings, and similarities with other users.

We can structure this project in different ways:

• Collaborative Filtering: Utilizes user-item interactions to recommend items. It can be implemented using techniques like User-based or Item-based collaborative filtering.
• Content-Based Filtering: Recommends items similar to those you have liked before based on content attributes like genre, actors, director, etc.
• Hybrid Approaches: Combines collaborative and content-based filtering for more accurate recommendations.

First, use a dataset containing user ratings, movie metadata, and user interactions. Second, p reprocess the data by handling missing values, normalizing ratings, or encoding categorical variables. Then, b uild recommendation models (such as matrix factorization, and k-nearest neighbors) using libraries like Surprise, Scikit-learn, or custom implementations.

Finally, evaluate the models using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or precision/recall.

17) Customer Segmentation

Customer Segmentation is also one of the projects based on data mining. It involves grouping customers based on similar characteristics, behaviors, or preferences to tailor marketing strategies or services.

Let’s take a brief look at the approach we have to use:

• RFM Analysis: It segments customers based on the recency, frequency, and monetary value of their purchases.
• Clustering Algorithms: Utilizes techniques like k-means clustering or hierarchical clustering to group customers based on features such as demographics, purchase history, or preferences.
• RFM and Demographic Fusion: Combines RFM analysis with demographic data for more refined segmentation.

It is also an amazing idea for Data Science projects that students can make.

18) Predicting Loan Defaulters

All the banks and organizations that lend money need to first assess the risk of loan default based on customer’s past data. To automate this task and save time, we can build a model to assess the risk of loan default based on applicant data and historical loan performance.

It is a simple model, and we can create in such simple steps:

• Collect and preprocess historical loan data including applicant details, loan amount, repayment status, etc.
• Split the dataset into training and testing sets.
• Train classification models on historical data and evaluate their performance using metrics like accuracy, precision, recall, or ROC-AUC.
• Use the trained model to predict the likelihood of default for new loan applications.

19) Web Click Prediction

Web Click Prediction involves using data mining techniques to predict or forecast user behavior on websites, particularly predicting what links or content a user is likely to click on. 

First collect the data on user behavior such as clickstreams, timestamps, referral sources, etc. Now, preprocess the data by cleaning it and extracting relevant features from the data that could be used for prediction (e.g., user demographics, browsing history, time of day, device used).

Employ the machine learning algorithms (such as decision trees, logistic regression, and neural networks) to build predictive models, and t rain the models using historical click data and relevant features.

20) Social Network Analysis

Everyone is very active on social media nowadays, and their behavior on these websites tells a lot about their preferences. We can utilize these data to identify communities, influencers, or patterns.

Social Network Analysis involves analyzing the relationships and connections among individuals or entities in a network. This project requires the following things:

• Graph Theory and Algorithms : Utilizes graph-based algorithms such as PageRank, community detection algorithms (like Louvain or Girvan-Newman), or centrality measures (like betweenness or closeness centrality).
• Network Visualization: Visualizes the network structure to understand the relationships and patterns visually.
• Influencer Identification: Identifies influential nodes or users in the network based on their connections and interactions.

Here, we will perform network analysis using libraries like NetworkX (in Python) or custom implementations in C++. After that, a pply graph algorithms to detect communities, find influential nodes, or analyze network properties.

Applications of Data Mining

Here are some major applications:

• Financial Analysis: The banking and finance industry relies on high-quality and processed, reliable data. In the finance industry user, data can be used for a variety of purposes, like portfolio management, predicting loan payments, and determining credit ratings.
• Telecommunication Industry: With the advent of the internet the telecommunication industry is expanding and growing at a fast pace. Data mining can help important industry players to improve their service quality to compete with other businesses.
• Intrusion Detection: Network resources can face threats and actions of cybercriminals can intrude on their confidentiality. Therefore, the detection of intrusion has proved as a crucial data mining practice. It enables association and correlation analysis, aggregation techniques, visualization, and query tools, which can efficiently detect any anomalies or deviations from normal behavior.
• Retail Industry: The established retail business owner maintains sizable quantities of data points covering sales, purchasing history, delivery of goods, consumption, and customer service. Database management has improved with the arrival of e-commerce marketplaces and emerging new technologies.
• Spatial Data Mining: Geographic Information Systems and many other navigation applications utilize data mining techniques to create a secure system for vital information and understand its implications. This new emerging technology includes the extraction of geographical, environmental, and astronomical data, extracting images from outer space.

How do I Start a Data Mining Project?

The first thing you would need to do is define a problem statement. Your project is only as good as your problem statement. Once you have defined a problem statement, gather data to solve the problem statement.

The data needs to be properly cleaned and in the format that you require it to be. After you have the data, run the data mining algorithms and visualize the results. This can help you gain insights from the data and help in choosing appropriate models to train the data on.

Best Ideas for Final Year Projects

You can choose ideas like Social Network Analysis, Web Click Prediction, and Air BNB Market Analysis for your first data mining project. As we know most students are making it to final year submission. These are very complex and require a lot of data and algorithms. 

Not only will these projects expand your understanding but also your teachers or supervisors will also favor such topics that are more related to the current times.

Now you have the list of Data Mining projects for beginners. So what are you waiting for, select one and start working on it. It is a composite discipline that can represent a variety of methods or techniques used in different analytic methods.

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In recent times, there is a massive growth in  information generation  through  “IoT.”  At the same time, it  stores  in  “Cloud Computing.” PhD Research Topics in Data Mining  is the academic stock of hot topics. It intends to convert our line of thoughts to your research As a result, it ‘ opens the way for research in data mining.’  Hence, join us to put your career on the right track of data mining. So that you will get ‘thrice times better success in your PhD.’

SOUNDFUL TOPICS

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• Data warehousing integration
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Our tireless pros from  PhD Research Topics in Data Mining  will uplift your research through their energetic ideas. On the whole, we are here to  polish each nook of your research . For this reason, we also work on apt selection of  simulation tools, datasets, and journals .

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PhD Research Topics in data Mining

Be Smart and Go With Our PhD Research Topics in Data Mining On the road to Huge Success!!!

Analysis  of Large-Scale Spatio-Temporal Data using Progressive Partition and Multidimensional Pattern Extraction

Recursive Event Sequence Exploration using Interweaving Queries and Pattern Mining

An Effective Minimum Spanning Tree Clustering for Anti-Noise Process Mining Algorithm

Visual Analytics of Scientific Data Sets using Graph-Based Techniques

An Analysis of Data Flow and Visualization for Spatiotemporal Statistical Data without Trajectory Information

Multimodal Data Correlation for Device Clustering Algorithm in Cognitive Internet of Things

Improved STRAP –Based Dynamic Clustering Scheme for Evolving Data Streams

Distributed storage system for electric power data using Hbase

Itemset Mining Methods for Detection of Frequent Alarm Patterns in Industrial Alarm Floods

An Efficient Algorithm for Clustering Categorical Data With Set-Valued Features

A Privacy Preserving in Multi-Access Edge Computing for Heterogeneous IoT over Big Data

Hidden Temporal Information and Rule-Based Entity Resolution on Database

A Automatic Fault Diagnosis and Prognosis for Distribution Automation using Data Analytic Methodology

Leveraging Graph Mining based on Compression for Behavior-Based Malware Detection

An Efficient IoT Enabled Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets

Cluster-Aided Wireless Channel Modeling based on Big Data Algorithms

IoT Enabled Three Hierarchical Levels of Big-Data Market Model in Multiple Data Sources

A Methodology to discovering companion patterns using traffic data stream

A Clustering based on Uncertain Data in Distributed Peer-to-Peer Networks

Grammar-Based Genetic Programming for Mining Context-Aware Association Rules

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data warehousing Recently Published Documents

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TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

Abstract Semi-stream join is an emerging research problem in the domain of near-real-time data warehousing. A semi-stream join is basically a join between a fast stream (S) and a slow disk-based relation (R). In the modern era of technology, huge amounts of data are being generated swiftly on a daily basis which needs to be instantly analyzed for making successful business decisions. Keeping this in mind, a famous algorithm called CACHEJOIN (Cache Join) was proposed. The limitation of the CACHEJOIN algorithm is that it does not deal with the frequently changing trends in a stream data efficiently. To overcome this limitation, in this paper we propose a TinyLFU-CACHEJOIN algorithm, a modified version of the original CACHEJOIN algorithm, which is designed to enhance the performance of a CACHEJOIN algorithm. TinyLFU-CACHEJOIN employs an intelligent strategy which keeps only those records of $R$ in the cache that have a high hit rate in S. This mechanism of TinyLFU-CACHEJOIN allows it to deal with the sudden and abrupt trend changes in S. We developed a cost model for our TinyLFU-CACHEJOIN algorithm and proved it empirically. We also assessed the performance of our proposed TinyLFU-CACHEJOIN algorithm with the existing CACHEJOIN algorithm on a skewed synthetic dataset. The experiments proved that TinyLFU-CACHEJOIN algorithm significantly outperforms the CACHEJOIN algorithm.

Large Scale System for Social Media Data Warehousing

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Understanding the Concept of Data Warehousing and Challenges in Its Implementation

The aim of this paper is to understand the concept of Data ware housing and how it is implemented. It is related to the data analysis of the data in an organisation. It facilitates and makes the analysis process easy for the workers of the organisation. The paper will also explain two approaches that are followed in data ware housing. The process of implementation of data ware house will also discussed further in this paper. There are certain challenges to create data ware house.

An Innovative Method to Extract Data in a Real-time Data Warehousing Environment

ETL (Extract, Transform, and Load) is an essential process required to perform data extraction in knowledge discovery in databases and in data warehousing environments. The ETL process aims to gather data that is available from operational sources, process and store them into an integrated data repository. Also, the ETL process can be performed in a real-time data warehousing environment and store data into a data warehouse. This paper presents a new and innovative method named Data Extraction Magnet (DEM) to perform the extraction phase of ETL process in a real-time data warehousing environment based on non-intrusive, tag and parallelism concepts. DEM has been validated on a dairy farming domain using synthetic data. The results showed a great performance gain in comparison to the traditional trigger technique and the attendance of real-time requirements.

Data Warehousing for Formula One (Racing) Popularity Rating Using Pentaho Tools

A framework for developing an enterprise data warehousing solution, developing a corporate data warehousing strategy, mapping the road to elimination: a 5-year evaluation of implementation strategies associated with hepatitis c treatment in the veterans health administration.

Abstract Background While few countries and healthcare systems are on track to meet the World Health Organization’s hepatitis C virus (HCV) elimination goals, the US Veterans Health Administration (VHA) has been a leader in these efforts. We aimed to determine which implementation strategies were associated with successful national viral elimination implementation within the VHA. Methods We conducted a five-year, longitudinal cohort study of the VHA Hepatic Innovation Team (HIT) Collaborative between October 2015 and September 2019. Participants from 130 VHA medical centers treating HCV were sent annual electronic surveys about their use of 73 implementation strategies, organized into nine clusters as described by the Expert Recommendations for Implementing Change taxonomy. Descriptive and nonparametric analyses assessed strategy use over time, strategy attribution to the HIT, and strategy associations with site HCV treatment volume and rate of adoption, following the Theory of Diffusion of Innovations. Results Between 58 and 109 medical centers provided responses in each year, including 127 (98%) responding at least once, and 54 (42%) responding in all four implementation years. A median of 13–27 strategies were endorsed per year, and 8–36 individual strategies were significantly associated with treatment volume per year. Data warehousing, tailoring, and patient-facing strategies were most commonly endorsed. One strategy—“identify early adopters to learn from their experiences”—was significantly associated with HCV treatment volume in each year. Peak implementation year was associated with revising professional roles, providing local technical assistance, using data warehousing (i.e., dashboard population management), and identifying and preparing champions. Many of the strategies were driven by a national learning collaborative, which was instrumental in successful HCV elimination. Conclusions VHA’s tremendous success in rapidly treating nearly all Veterans with HCV can provide a roadmap for other HCV elimination initiatives.

Explicitly Disclosing Clients Illness Catalogue Using Data Science Techniques

Abstract: Across the world in our day-to-day life, we come across various medical inaccuracies caused due to unreliable patient’s reminiscence. Statistically, communication problems are the most significant aspect that hampers the diagnosis of patient’s diseases. So, this paper represents the best theoretical solution to achieve patient care in the most adequate way. In these pandemic days, the communication gap between the patient and the physician has begun to decline to a nominal level. This paper demonstrates a vital solution and a steppingstone to the complete digitalization of the client’s illness catalogue. To attain the solution in a specified manner we are using adverse pre-existential technologies like data warehousing, database management system, cloud computing, big data, etc. We also persistently maintain the most secure, impenetrable infrastructure enabling the client’s data privacy. Keywords: Illness catalogue, cloud computing, data warehousing, database management systems, big data.

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Creating a Corporate Social Responsibility Program with Real Impact

• Emilio Marti,
• David Risi,
• Eva Schlindwein,
• Andromachi Athanasopoulou

Lessons from multinational companies that adapted their CSR practices based on local feedback and knowledge.

Exploring the critical role of experimentation in Corporate Social Responsibility (CSR), research on four multinational companies reveals a stark difference in CSR effectiveness. Successful companies integrate an experimental approach, constantly adapting their CSR practices based on local feedback and knowledge. This strategy fosters genuine community engagement and responsive initiatives, as seen in a mining company’s impactful HIV/AIDS program. Conversely, companies that rely on standardized, inflexible CSR methods often fail to achieve their goals, demonstrated by a failed partnership due to local corruption in another mining company. The study recommends encouraging broad employee participation in CSR and fostering a culture that values CSR’s long-term business benefits. It also suggests that sustainable investors and ESG rating agencies should focus on assessing companies’ experimental approaches to CSR, going beyond current practices to examine the involvement of diverse employees in both developing and adapting CSR initiatives. Overall, embracing a dynamic, data-driven approach to CSR is essential for meaningful social and environmental impact.

By now, almost all large companies are engaged in corporate social responsibility (CSR): they have CSR policies, employ CSR staff, engage in activities that aim to have a positive impact on the environment and society, and write CSR reports. However, the evolution of CSR has brought forth new challenges. A stark contrast to two decades ago, when the primary concern was the sheer neglect of CSR, the current issue lies in the ineffective execution of these practices. Why do some companies implement CSR in ways that create a positive impact on the environment and society, while others fail to do so? Our research reveals that experimentation is critical for impactful CSR, which has implications for both companies that implement CSR and companies that externally monitor these CSR activities, such as sustainable investors and ESG rating agencies.

• EM Emilio Marti is an assistant professor at the Rotterdam School of Management (RSM) at Erasmus University Rotterdam.
• DR David Risi is a professor at the Bern University of Applied Sciences and a habilitated lecturer at the University of St. Gallen. His research focuses on how companies organize CSR and sustainability.
• ES Eva Schlindwein is a professor at the Bern University of Applied Sciences and a postdoctoral fellow at the University of Oxford. Her research focuses on how organizations navigate tensions between business and society.
• AA Andromachi Athanasopoulou is an associate professor at Queen Mary University of London and an associate fellow at the University of Oxford. Her research focuses on how individuals manage their leadership careers and make ethically charged decisions.

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