Title:

Managing Data with High Redundancy and Low Value Density

Abstract:

Machine-to-machine (M2M) data, often found in sensor networks, GPS and RFID applications, vehicle on-board devices and medical monitoring devices, is the next generation of data we need to manage and process. In addition to large volumes and streaming nature, such data typically have high level of redundancy and low value density. There is a need to develop a new breed of database management systems that can support stream query processing as well as managing historical data to support complex data analytics, data mining and data-driven decision making. In this talk we advocate a novel database approach to data storage, cleaning, compression, hierarchical summation, indexing and query processing for machination data.

Bio:

Professor Xiaofang Zhou is a Professor of Computer Science at The University of Queensland, Australia, leading the Data and Knowledge Engineering (DKE) Group at UQ. His research focus is to find effective and efficient solutions for managing, integrating and analyzing very large amount of complex data for business, scientific and personal applications. He has been working in the area of spatial and multimedia databases, data quality, high performance database systems and data mining. He is a Program Committee Chair for IEEE ICDE 2013, CIKM 2016 and a General Chair of ACM Multimedia 2015. He has been an Associate Editor of The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, World Wide Web Journal, Distributed and Parallel Databases, and IEEE Data Engineering Bulletin. He is the current Chair of IEEE Technical Committee on Data Engineering (TCDE).

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