Data mining has been a trending research area with contributions from diverse communities including computer scientists, statisticians, mathematicians, and researchers working on data-intensive problems. While most data mining methodologies are developed for general problem settings, such as unsupervised learning and supervised learning, (1) there are many factors and challenges such as socioeconomic, organizational, human-centered and cultural aspects rarely explored; (2) there are also specific domain knowledge, factors and challenges in developing data mining solutions for a specific domain or a novel real-world application; and (3) a critical challenge facing existing data mining is to discover actionable knowledge that can directly support decision-making tasks. Due to the need of incorporating such domain knowledge, factors and challenges in the data mining process, the challenge to discover actionable knowledge hidden in complex data, and the lack of both general and customized algorithms and tools, domain driven data mining presents many significant challenges and opportunities for transforming data mining to actionable knowledge discovery and for delivering actionable insights and intelligence for solving general and specific domain-driven problems. This special issue aims 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.
Indeed, Domain-Driven Data Mining has attracted significant attention from both academic and industry. There have been a workshop series on domain driven data mining during 2007-2011 with ICDM which has been competitively ranked in the Australia CORE conference rank, a special issue with TKDE, and several tutorials delivered. Given that the previous activities were mostly 10 years ago, there are various new research problems and challenges following the recent advances in the last decade. Building upon these prior works, we aim to foster further discussions on broad general and specific domain challenges, the incorporation of domain knowledge into data mining processes and models, especially methodologies and tools newly developed in the last decade, such as deep neural networks, graph embedding, text mining, and reinforcement learning.
This workshop will be part of SDM 2021. Domain-driven data mining is an important research in SDM given the highly applied and interdisciplinary nature of the conference. In order for data mining algorithms to achieve superior performance in significant business and societal problems, it is necessary to incorporate domain knowledge to guide the model and algorithm design.
Accepted papers/presentations can be further reviewed for journal publications:
This workshop aims to attract target audience including researchers and professionals from various interdisciplinary domains, such as earth science, biomedical science, urban planning, transportation, public safety, etc. The workshop aims to attract researchers who are interested in leveraging domain specific knowledge in designing customized data mining algorithms. In addition, the workshop will also attract researchers who are interested in generalizing data mining algorithms from one domain to another domain (i.e., cross-domain data mining). We hope this workshop can bring together leading researchers and practitioners to share their experiences and latest research/application results on all aspects of Domain-driven Data Mining.
The topics of interest include but not limited to:
The program will feature keynote talks, accepted papers, and posters. Since the workshop will be virtual, the talks and presentations will be delivered online in interactive sessions. Recordings will be made available to accommodate participates from different time zones.
May 1st, Saturday, 2021, Eastern Time (ET)
Professor in Information Technology
Faculty of Engineering and IT
University of Technology Sydney, Australia
Anderson Professor of Global Management
Muma College of Business
University of South Florida, USA
ACM Distinguished Scientist, IEEE Fellow
Management Science and Information Systems
Rutgers University, USA
Distinguished Professor, Wexler Chair in Information Technology
University of Illinois at Chicago
University of Tennessee
University of Alabama
University of Science and Technology of China
Philip S. Yu, University of Illinois at Chicago
Contact Information: 851 S. Morgan St., Rm 1138 SEO, Chicago, IL 60607
Philip S. Yu's main research interests include data mining, privacy preserving publishing and mining, data streams, database systems, Internet applications and technologies, multimedia systems, parallel and distributed processing, and performance modeling. He is a Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.
Dr. Yu is a Fellow of the ACM and of the IEEE. He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery from Data. He is on the steering committee of IEEE Conference on Data Mining and was a member of the IEEE Data Engineering steering committee. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004), an editor, advisory board member and also a guest co-editor of the special issue on mining of databases. He had also served as an associate editor of Knowledge and Information Systems. In addition to serving as program committee member on various conferences, he was the program chair or co-chairs of the IEEE Workshop of Scalable Stream Processing Systems (SSPS'07), the IEEE Workshop on Mining Evolving and Streaming Data (2006), the 2006 joint conferences of the 8th IEEE Conference on E-Commerce Technology (CEC' 06) and the 3rd IEEE Conference on Enterprise Computing, E-Commerce and E-Services (EEE' 06), the 11th IEEE Intl. Conference on Data Engineering, the 6th Pacific Area Conference on Knowledge Discovery and Data Mining, the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, the 2nd IEEE Intl. Workshop on Research Issues on Data Engineering: Transaction and Query Processing, the PAKDD Workshop on Knowledge Discovery from Advanced Databases, and the 2nd IEEE Intl. Workshop on Advanced Issues of E-Commerce and Web-based Information Systems. He served as the general chair or co-chairs of the 2006 ACM Conference on Information and Knowledge Management, the 14th IEEE Intl. Conference on Data Engineering, and the 2nd IEEE Intl. Conference on Data Mining. He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 93rd plateau of Invention Achievement Awards. He was an IBM Master Inventor. Dr. Yu received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and perpetuating numerous new electrical engineering concepts" in 1999.
Chuanren Liu, University of Tennessee
Contact Information: 916 Volunteer Blvd, Knoxville, TN 37996
Dr. Chuanren Liu is an Assistant Professor of Business Analytics and Statistics at the University of Tennessee, Knoxville. His research interests include data mining and knowledge discovery, and their applications in business analytics. He has published papers in refereed journals and conference proceedings, such as IEEE Transactions on Data and Knowledge Engineering, INFORMS Journal on Computing, European Journal of Operational Research, Annals of Operations Research, IEEE Transactions on Cybernetics, Knowledge and Information Systems and KDD, ICDM, SDM, AAAI, IJCAI, UbiComp, IEEE BigData, etc. He served on the Editorial Board for the journal of Electronic Commerce Research and Applications, the Registration Chair for the IEEE International Conference on Data Mining, and a Senior Program Committee member for AAAI Conference on Artificial Intelligence.
Zhe Jiang, University of Alabama
Contact Information: Cyber Hall 3054, Box 870290, Tuscaloosa, AL 35487
Dr. Zhe Jiang is an assistant professor in the department of Computer Science at the University of Alabama, Tuscaloosa. He received his Ph.D. in computer science from the University of Minnesota, Twin Cities in 2016, and B.E. in electrical engineering from the University of Science and Technology of China, Hefei, China, in 2010. His research interests include spatial big data analytics, spatial and spatio-temporal data mining, spatial database, geographic information system, and their interdisciplinary applications in earth science, transportation, public safety, etc. Dr. Jiang's research has been published in prestigious data mining conferences and journals, including ACM SIGKDD, IEEE TKDE, and IEEE ICDM. He received the NSF CRII award for his work on ``Disciplinary Knowledge Guided Big Spatial Structured Models for Geoscience Application".
Tong Xu, University of Science and Technology of China
Contact Information: School of Computer Science, West Campus, USTC, Hefei, Anhui, P.R.China, 230027
Dr. Tong Xu is currently working as an Associate Researcher at the University of Science and Technology of China (USTC), Hefei, China. He also serves as a committee member of the Expert Committee of Social Media Processing, Chinese Information Processing Society of China. He has authored 40+ top-tier journal and conference papers in the fields of domain-driven data mining, including IEEE TKDE, IEEE TMC, KDD, WWW, ICDM, SDM, etc. Besides, he has served on numerous conferences, such as a session chair of CCKS 2018, and the program committee member of KDD, AAAI, SDM, EMNLP, etc. He was nominated as the Distinguished Dissertation Award of China Association for Artificial Intelligence (2018). He served as the co-organizer for the workshop on Organizational Behavior and Talent Analytics (OBTA) in KDD 2018, as well as the workshop on Talent and Management Computing (TMC) in KDD 2019.
We expect that this workshop could receive wide attention of attendees with interdisciplinary background, which leads to at least 40-50 attendances.
We invite the submission of regular research papers (6-9 pages), as well as vision papers and short technical papers (around 4 pages), including all content and references. Papers must be in PDF format, and formatted according to the SDM template.
To encourage the discussion, both original papers, and papers which have been published before, are all welcome to be submitted to this workshop. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insights, depth, clarity, and reproducibility. Considering the practical characters of this workshop, to enrich the presentations, we strongly encourage the authors to submit their demonstrations, e.g., intelligent system for talent analytics, which will also be evaluated during the review process.
All the papers are required to be submitted via EasyChair system.
For more questions about the workshop and submissions, please send email to firstname.lastname@example.org.
Accepted papers/presentations can be further reviewed for journal publications.
(February 28), 2021: Paper submission (23:59, Anywhere on Earth)
March 31, 2021: Paper notification
April 29 - May 1, 2021: Workshop and SDM Virtual Conference