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Computer Science Department Colloquium
10/2/2018 10:30 am
CoRE A 301

Sequential Pattern Analysis with the Right Granularity

Professor Hui Xiong, Management Science and Information Systems Department, Rutgers Business School

Faculty Host: Matthew Stone


Sequential pattern analysis aims at finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields. Given the overwhelming scale and the dynamic nature of the sequential data, new visions and strategies for sequential pattern analysis are required to derive competitive advantages and unlock the power of the big data. To this end, in this talk, we present novel approaches for sequential pattern analysis with applications in dynamic business environments. Particularly, we focus on the development of “temporal skeletonization”, which can help to identify the meaningful granularity for sequential pattern mining. Along this line, we first show that a large number of symbols in a sequence can “dilute” useful patterns which themselves exist at a different level of granularity. This is so-called “curse of cardinality”, which can impose significant challenges to the design of sequential analysis methods. To address this challenge, our key idea is to summarize the temporal correlations in an undirected graph, and use the “skeleton” of the graph as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Evaluation on a B2B (Business to Business) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.


Dr. Hui Xiong is currently a Full Professor in the Management Science and Information Systems Department at Rutgers, the State University of New Jersey, where he received RBS Dean’s Research Professorship (2016), a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the Best Research Paper Award at the 2011 IEEE International Conference on Data Mining (ICDM).  For his outstanding contributions to data mining and mobile computing, he was elected an ACM Distinguished Scientist in 2014.

Dr. Xiong is a prominent researcher in the areas of business intelligence, data mining, and big data. He has a distinguished academic record that includes 200+ referred papers in conference proceedings and journals, and an authoritative Encyclopedia of GIS (Springer). He is serving on the editorial boards of ACM Transactions on Knowledge Discovery from Data (TKDD), ACM Transactions on Management Information Systems (TMIS), and IEEE Transactions on Big Data. Also, he served as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), a General Co-Chair for the IEEE 2015 International Conference on Data Mining (ICDM-2015), and a Program Co-Chair of the Research Track for the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2018).

Dr. Xiong is committed to data science education. He has graduated 11 PhD students, and most of them are now faculty members in major research universities in USA, such as the University of Tennessee – Knoxville, the University of Arizona – Tucson, Stony Brook University, George Mason University, and Drexel University. Homepage: