Big data applications have become indispensable in as diverse fields as the environment, commerce, social science and geography. The 3 “V”s (Volume, Velocity and Variety) have caused fundamental challenges to the ways traditional data management systems are designed and implemented as well as how data is consumed and analyzed. Major Privacy concerns arise when big data is managed and stored remotely in the Cloud. In this talk, we will explore some of the system challenges arising from social media and environmental applications. In large social media, we will explore some of the challenges in understanding, managing and analyzing the diffusion of information in diverse settings. We will address some of the practical privacy concerns arising in environmental applications, and how the collective good can benefit from efficient secure data methods. We will also highlight some of the main volume challenges of big data as the data scales out in large data centers as well as in multiple data centers for fault-tolerance in the face of catastrophic failures.

first ACM Conference on Social Networks (COSN) 2013. He currently serves on the executive committee of the IEEE Technical Committee on Data Engineering (TCDE) and was a board member of the VLDB Endowment from 2002 to 2008. In 2007, Prof. El Abbadi received the UCSB Senate Outstanding Mentorship Award for his excellence in mentoring graduate students. In 2013, his student, Sudipto Das received the SIGMOD Jim Gray Doctoral Dissertation Award. He has published over 300 articles in databases and distributed systems and has supervised over 30 PhD students.

The ever-growing social networks and social media provide invaluable sources of information for modeling the behavior of users. High-quality user models enable superior services and functions for end users. In this talk, I will present several examples of user modeling based on social networks and social media. I will describe our research in modeling users'
information preferences on Microblogs using a novel user message model and discuss our work on extracting users' daily activities, such as dining and shopping, that inherently reflect their habits, intents and preferences. I explain our novel transfer learning solution via a collaborative boosting framework comprising a text-to-activity classifier for socially connected users. I will also describe our research on user modeling in multiple, overlapping social networks in a “composite social network” setting. Finally, I will explain our research on finding social spammers in large social networks.

several top international conferences and is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST). He has organized several top conferences in AI and Data Mining, including KDD 2010 and 2012, and International Joint AI Conference in 2015.