Colloquium: Interactive Large-Scale Data Management and Analysis
Abstract: Nowadays, different forms of data are collected, stored, and analyzed to support important business and scientific tasks. As the amount of data grows large and fast, we face unprecedented challenges in large scale data management and analysis. In particular, we have to manage and analyze large-scale data sets interactively to provide timely insights for real-world applications. Usually, such data can be much larger than a single machine capcity and are collected in various formats at extremely high speed. Towards achieving interactive experience, algorithms and systems have to be designed carefully to match up the trend of data. Furthermore, there is a growing interest in outsourcing storage and computing in public cloud, which raises concerns in security and privacy. In this talk, I will go through some of my past works in building interactive data analysis systems, including the in-memory distributed spatial analysis engine Simba and a new storage layer FishStore supporting real-time data analysis over incoming un-/semi-structured data. Besides, my other works on cloud security and data privacy will be covered briefly. Finally, I will describe future research opportunities in this area such as real-time data analysis engine that I am passionate about.
Bio: Dong Xie is a PhD candidate from data group at University of Utah. He received his bechalor degree from ACM Hornored Class at Shanghai Jiao Tong University in 2015, and currently work with Prof. Feifei Li. Dong is a 2018 Microsoft Research PhD Fellow and received other rewards like SoCC 2019 best paper runner-up. His research lies in different aspect of data management, including distributed database system, approximate query processing, security, and data privacy.
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