The purpose of the 6th International Conference on Data Science, Technology and Applications (DATA) is to bring together researchers, engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data. Conference Areas 1 . Business Analytics2 . Data Management and Quality3 . Big Data4 . Databases and Data Security Conference CHAIR Joaquim Filipe, Polytechnic Institute of Setúbal / INSTICC, Portugal PROGRAM CO-CHAIRs Jorge Bernardino, Polytechnic Institute of Coimbra - ISEC, PortugalChristoph Quix, RWTH Aachen University, Germany Keynote Speakers Managing Big Multidimensional Data A Journey From Acquisition to Prescriptive Analytics Torben Bach Pedersen Aalborg University Denmark Brief 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 logistics (indoor/outdoor moving objects), smart grids (energy data management), transport (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 Information Systems and Springer EDBS, PC Chair for DaWaK, DOLAP, SSDBM, and DASFAA, and regularly serves on the PCs of the major database conferences. Abstract More and more data is being collected from a variety of new sources such as sensors, smart devices, social media, crowd-sourcing, and (Linked) Open Data. Such data is large, fast, and often complex. There is a universal wish perform multidimensional OLAP-style analytics on such data, i.e., to turn it into "Big Multidimensional Data". The keynote will look at challenges and solutions in managing Big Multidimensional data. This is a multi-stage journey from its initial acquisition, over cleansing and transformation, to (distributed) storage, indexing, and query processing, further on to building (predictive) models over it, and ultimately performing prescriptive analytics that couples analytics with optimization to suggest optimal actions. A number of case studies from advanced application domains such as Smart Energy, Smart Transport, and Smart Logistics will be used for illustration. To be announced soon. Francesco Bonchi ISI Foundation Italy Brief Bio Francesco Bonchi is Research Leader at the ISI Foundation, Turin, Italy, where he leades the "Algorithmic Data Analytics" group. He is also (part-time) Principal Scientist for Data Mining at Eurecat (Technological Center of Catalunya), Barcelona. Before he was Director of Research at Yahoo Labs in Barcelona, Spain, where he was leading the Web Mining Research group. His recent research interests include mining query-logs, social networks, and social media, as well as the privacy issues related to mining these kinds of sensible data. In the past he has been interested in data mining query languages, constrained pattern mining, mining spatiotemporal and mobility data, and privacy preserving data mining. He is member of the ECML PKDD Steering Committee, Associate Editor of the newly created IEEE Transactions on Big Data (TBD), of the IEEE Transactions on Knowledge and Data Engineering (TKDE), the ACM Transactions on Intelligent Systems and Technology (TIST), Knowledge and Information Systems (KAIS), and member of the Editorial Board of Data Mining and Knowledge Discovery (DMKD). He has been program co-chair of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010). Dr.
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