Location: CSA Seminar Room No. 254
Lecture Title : Data-Driven Analysis: Decomposition-Based Approach for Multilayer Networks (MLNs)
Date and Time : 7th June, 2019 (Friday), 11:00 AM – 12:00 PM
We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (Big Data Analytics/Science) along with new approaches to benefit humanity at large in the best possible way. This warrants developing and/or using new approaches — technological, scientific, and systems — in addition to building upon and integrating with the ones that have been developed so far. With this ambitious goal, there is also the risk of these advancements being misused or abused as we have seen so many times with respect to new technologies.
In the first part of the presentation, we take the audience on a retrospective stroll on the approaches that have come about for managing and analyzing data over the last 40+ years. Since the advent of Database Management Systems (or DBMSs) and especially the Relational DBMSs (or RDBMSs), data management and analysis have seen several significant strides. Today, data has become an important tool (or even a weapon) in society and its role and importance is unprecedented.
Modeling and analysis of complex data sets (i.e., data sets with multiple entity and feature types along with their relationships) are challenging especially if one wants to do it in a flexible and efficient way to match the analysis objectives. Analysis entails understanding of the data set with respect to entity and feature combinations as well as inferring actionable knowledge. We posit that modelling of these data sets is equally important and can be done elegantly using multilayer networks (or multiplexes — layers of networks that are inter-connected) instead of using a single graph. Recent research is towards efficient and scalable approaches for this representation.
In second part of this talk, we first illustrate the elegance of multilayer networks for modelling by using a few well-known data sets. Flexibility of analysis comes from this approach to modelling. Then we discuss the decomposition approach for computation which is efficient in that it combines communities (and hubs) from individual layers to form loss-less communities for any combination of layers in a multilayer network. Finally, we present several diverse case studies to showcase the approach.
Prof. Chakravarthy is an ACM Distinguished Scientist and an ACM Distinguished Speaker. He is also an IEEE Senior Member. He organized (General Co-Chair) the 13th international Conference on Distributed Event-Based Systems (DEBS) in 2013 at UT Arlington. He spent the summers of 2013, 2014, and 2017 at the Rome Air Force Research Laboratory (AFRL-Rome) working on, respectively, continuous query processing over fault-tolerant networks and applying stream processing framework to video stream analysis. He is a co-author of the book “Stream Data Processing: A Quality of Service Perspective” published by springer in 2009.
Sharma Chakravarthy is Professor of Computer Science and Engineering Department at The University of Texas at Arlington, Texas since January 2000. He established the Information Technology Laboratory (IT Lab) at UT Arlington in Jan 2000. Sharma Chakravarthy has also established the NSF funded, Distributed and Parallel Computing Cluster at UT Arlington. He is the recipient of the university-level “Creative Outstanding Researcher” award for 2003 and the department level senior outstanding researcher award in 2002.
He is well known for his work on stream data processing, semantic query optimization, multiple query optimization, active databases (HiPAC project at CCA and Sentinel project at the University of Florida, Gainesville), and more recently scalability issues in graph mining, social network analysis, and graph analysis of multilayered networks. His group at UTA is currently adapting map/reduce and other paradigms for scaling graph mining algorithms to very large graphs and for answering graph queries. He has applied machine learning techniques to rank answers, identify general- and topic-based experts in a Question-Answer (or Q-A) social network. His work on InfoSift – a classification system for text, email, and web – has used graph mining techniques.
His current research includes big data analysis using multi-layered networks, stream data processing for disparate domains (e.g., video analysis), scaling graph mining algorithms for analyzing very large social and other networks, active and real-time databases, distributed and heterogeneous databases, query optimization (single, multiple, logic-based, and graph), and multi-media databases. He has published over 200 papers/book chapters in refereed international journals and conference proceedings. He has supervised 15 PhD theses and 100+ MS thesis. He has given tutorial on a number of database topics, such as graph mining, active, real-time, distributed, object-oriented, and heterogeneous databases in North America, Europe, and Asia. He is listed in Who’s Who Among South Asian Americans and Who’s Who Among America’s Teachers.
Prior to joining UTA, he was with the University of Florida, Gainesville for 10 years. Prior to that, he worked as a Computer Scientist at the Computer Corporation of America (CCA) for 3 years and as a Member, Technical Staff at Xerox Advanced Information Technology, Cambridge, MA for a year.
Sharma Chakravarthy received the B.E. degree in Electrical Engineering from the Indian Institute of Science (IISc), Bangalore and M.Tech from IIT Bombay, India. He worked at TIFR (Tata Institute of Fundamental Research), Bombay, India for a few years. He received M.S. and Ph.D degrees from the University of Maryland in College park in 1981 and 1985, respectively.
All are welcome.