Abstract:In the big data era, data streams with huge volumes and fast generation rates become prevalent in many real-world applications such as social networks, finance, and Internet of things. It is challenging to extract useful information from massive data streams. One important approach to tackling the challenges that has attracted a lot of interest recently is data stream summarization that selects representative subsets of manageable sizes out of large data streams on the fly. In this talk I will review the recent advances on streaming submodular maximization, one of the most important methods for data stream summarization. I will introduce the models, applications, and algorithmic techniques for streaming submodular maximization. Furthermore, I will also briefly review other methods for data stream summarization, e.g., coresets and sketches. Finally, I will discuss several promising future directions on data stream summarization.

Speakers:  Yanhao Wang
Yanhao Wang is a postdoctoral researcher at the Department of Computer Science, University of Helsinki working in the Algorithmic Data Science (ADS) group (https://www2.helsinki.fi/en/researchgroups/algorithmic-data-science). He obtained his Ph.D. degree in computer science from National University of Singapore in 2020. His research interest includes data stream mining, algorithmic fairness, and machine learning for databases.

Affiliation:  University of Helsinki

Place of Seminar:  Zoom (Available afterwards on Youtube)

Meeting ID: 667 0703 4315

Passcode: 176758