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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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Chuanren Liu

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Ehsan Fakharizadi

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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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Published : 27 December 2022

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DOI : https://doi.org/10.1007/s41060-022-00378-1

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