Torben Bach Pedersen, Professor of Computer Science
Data collected from new sources such as sensors and smart devices is large, fast, and often complex. There is a universal wish to perform multidimensional OLAP-style analytics on such data, i.e., to turn it into “Big Multidimensional Data.” Supporting this is a multi-stage journey, requiring new tools and systems, and forming a new, extended data cycle with models as a key concept. We will look at three specifics steps in this data cycle.
First, we will look at model-based data acquisition and cleansing for indoor positioning data. Then we will move on to model-based distributed storage and query processing for large and fast time series in the ModelarDB system. Finally, we will present SolveDB, a SQL-based tool supporting a new type of analytics, prescriptive analytics, that integrates descriptive and predictive analytics with optimization problem solving to prescribe optimal actions. Application domains such as Smart Logistics and Smart Energy are used for illustration.
Bio: Torben Bach Pedersen is a Professor of Computer Science at Aalborg University, Denmark. His research interests include many aspects of Big Data analytics, with a focus on technologies for “Big Multidimensional Data” — the integration and analysis of large amounts of complex and highly dynamic multidimensional data in domains such as smart energy (energy data management), logistics and transport (moving objects and GPS data), and Linked Open Data.
He is an ACM Distinguished Scientist, and a member of the Danish Academy of Technical Sciences, the SSTD Endowment, and the SSDBM Steering Committee. He has served as Area Editor for IEEE Transactions on Big Data, Information Systems and Springer EDBS, PC Chair for DaWaK, DOLAP, SSDBM, and DASFAA, and regularly serves on the PCs of the major database conferences like SIGMOD, PVLDB, ICDE and EDBT. He received Best Paper/Demo awards from ACM e-Energy and WWW.