Society Enabled by Big Data

Digital information about users is undoubtedly the oil of the new economy. Collecting, processing, and leveraging such data at large scale, a trend called Big Data, is the fuel that powers many online services like Facebook, Google, Amazon and Netflix. While Big Data has a tremendous potential, it also raises a severe concern for users’ privacy. Our main research interests focus on these two sides of Big Data.

Understanding, predicting and shaping human behavior

Big Data hold many promises, not only for the individual but also for the public good. At the individual level, Big Data can help users to become more connected, productive, and entertained. At the society level, Big Data creates tremendous opportunities in areas ranging from marketing to public health and urban planning. Our work in this area has focused on utilizing data to build computational models of human behavior. Having such models would then allow us to better understand and predict future behaviors, and ultimately intervene when needed.

Privacy and security aspects of personal data

A series of privacy incidents over the last few years have focused public attention on how governments, businesses and other entities collect vast amounts of data about people’s lives and how that information is analyzed and used. Such concerns over privacy and data protection tamper the tremendous opportunities and extraordinary benefits of Big Data. Finding the right balance between privacy risks and rewards remains a great challenge. My own work in this area has focused on investigating technological means of protecting personal data while keeping them as useful as possible.

Equipment

The Big Data Lab is equipped with a computation cluster consisting of 8 powerful servers. Our cluster includes 128 cores, 1024 GB of RAM and 384 TB of secondary storage.

Wanted

Are you the next generation of researchers and big data scientists?
We are currently recruiting outstanding graduate students and postdocs…

Featured Publications

A Personal Data Store Approach for Recommender Systems: Enhancing Privacy without Sacrificing Accuracy

Journal Publication
I. Mazeh and E. Shmueli: "A Personal Data Store Approach for Recommender Systems: Enhancing Privacy without Sacrificing Accuracy". Expert Systems with Applications, 2019.
Publication year: 2019

Structural Entropy: Monitoring Correlation-Based Networks Over Time With Application To Financial Markets

Journal Publication
A. Almog and E. Shmueli: "Structural Entropy: Monitoring Correlation-Based Networks Over Time With Application To Financial Markets". Scientific Reports, 2019.
Publication year: 2019

Beyond Majority: Label Ranking Ensembles based on Voting Rules

Journal Publication
H. Werbin, L. Dery and E. Shmueli: "Beyond Majority: Label Ranking Ensembles based on Voting Rules". Expert Systems with Applications, 2019.
Publication year: 2019

Timing Matters: Influence Maximization in Social Networks through Scheduled Seeding

Journal Publication
D. Goldenberg, A. Sela and E. Shmueli: “Timing Matters: Influence Maximization in Social Networks through Scheduled Seeding”. IEEE Transactions on Computational Social Systems, 2018.
Publication year: 2018

Money Drives: Can Monetary Incentives based on Real-Time Monitoring Improve Driving Behavior?

Journal Publication
Y. Cohen and E. Shmueli: "Money Drives: Can Monetary Incentives based on Real-Time Monitoring Improve Driving Behavior?". IMWUT/Ubicomp, 2018.
Publication year: 2017