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User Group Analytics: Discovery, Exploration and Visualization

Half-day tutorial at CIKM 2018- Friday, 26 October 2018

Behrooz Omidvar-Tehrani

University of Grenoble Alpes, France

Behrooz Omidvar-Tehrani is a postdoctoral researcher in the University of Grenoble Alpes, France. Previously, he was a post-doctoral researcher at the Ohio State University, USA. His research is in the area of data management, focusing on interactive analysis of user data. Behrooz received his PhD in Computer Science from University of Grenoble Alpes, France. He has published in several international conferences and journals including CIKM, ICDE, VLDB, EDBT and KAIS. Also, he has been a reviewer for several conferences and journals including Information Systems, TKDE, DAMI, CIKM, ICDE, and AAAI.

Sihem Amer-Yahia

Laboratoire d’Informatique de Grenoble, CNRS, France

Sihem Amer-Yahia is a Research Director at LIG in Grenoble where she leads the SLIDE team. Her interests are at the intersection of large-scale data management and user data exploration. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Sta at at&t Labs. Sihem served on the SIGMOD Executive Board, the VLDB Endowment, and the EDBT Board. She is the Editor-in-Chief of the VLDB Journal and has been on the editorial boards of TODS and the Information Systems Journal. She is chairing VLDB 2018 and WWW Tutorials 2018 and will be chairing ICDE Tutorials 2019 and WWW Workshops 2019. Sihem received her Ph.D. in CS from Paris-Orsay and INRIA in 1999, and her Diplôme d’Ingénieur from INI, Algeria.



Abstract

User data is becoming increasingly available in various domains from the social Web to patient health records. User data is characterized by a combination of demographics (e.g., age, gender, occupation) and user actions (e.g., rating a movie, following a diet). Domain experts rely on user data to conduct large-scale population studies. Information consumers rely on the social Web for routine tasks such as finding a book club. User data analytics is usually based on identifying group-level behaviors such as “countryside teachers who watch Woody Allen movies.” User Group Analytics (UGA) addresses peculiarities of user data such as noise and sparsity. This tutorial reviews research on UGA and discusses different approaches and open challenges for group discovery, exploration, and visualization.



Detailed Outline

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