Lindner Hotel Prague Castle
Prague, Czech Republic
Photos taken by Milena
Photos taken by Petr
Main Conference Dates
April 10, 2016 - Submission of papers (extended)
May 15, 2016 - Notification of acceptance
June 12, 2016 - Camera-ready papers
August 29-31, 2016 - Conference
Conference allied with ADBIS 2016
BIR - Prague 14.- 16. 9. 2016
Prof. Avigdor Gal
Affiliation: Technion Israel Institute of Technology, Faculty of Industrial Engineering & Management
Abstract: The evolution of data accumulation, management, analytics, and visualization has recently led to coining the term big data. Big data encompasses technological advancement such as Internet of things (accumulation), cloud computing (management), and data mining (analytics), packaging it all together while providing an exciting arena for new and challenging research agenda. In the light of these landscape changes we analyze in this talk the impact of big data on data integration, which involves the alignment of distributed, heterogeneous, and autonomously evolving data. Big data integration is about matching social media with sensor data, putting it into use in applications such as smart city, health informatics, etc. In particular, the talk will present advancement in automatic tools for data integration and the changing role of human experts.
Prof. Dr. Erhard Rahm studied Computer Science at the University of Kaiserslautern (diploma 1984, Ph.D. 1988, habilitation 1993). From 1988 to 1989 he was a post-doctoral fellow at the IBM Research Center in Hawthorne, NY. Since 1994 he is a Full Professor for databases at the University of Leipzig. He spent sabbaticals at Microsoft Research (Redmond, WA) and the Australian National University. His current research focusses on Big Data and data integration. He has authored several books and more than 200 peer-reviewed journal and conference publications. His research on data integration has been awarded several times, in particular with the renowned 10-year best-paper award of the conference series VLDB (Very Large Databases) and the Influential Paper Award of the conference series ICDE (Int. Conf. on Data Engineering).
Prof. Rahm is scientific co-coordinator of the new German center of excellence on Big Data ScaDS (competence center for SCAlable Data services and Solutions) Dresden/Leipzig.
Prof. Erhard Rahm
Affiliation: University of Leipzig
Abstract: Data integration is a key challenge for Big Data applications to semantically enrich and combine large sets of heterogeneous data for enhanced data analysis. In many cases, there is also a need to deal with a very high number of data sources, e.g., product offers from many e-commerce websites. We will discuss approaches to deal with the key data integration tasks of (large-scale) entity resolution and schema or ontology matching. In particular, we discuss parallel blocking and entity resolution on Hadoop platforms together with load balancing techniques to deal with data skew. We also discuss challenges and recent approaches for holistic data integration of many data sources, e.g., to create knowledge graphs or to make use of huge collections of web tables.
Prof. Pavel Zezula is a professor of Informatics at the Masaryk University, Brno, Czech Republic. He spent more than ten years in Italy, mainly cooperating with the Italian National Research Council in Pisa and the University of Bologna. He is a coauthor of the Similarity Search: The Metric Space Approach book, published by Springer US. He has published more than 100 peerreviewed conference papers and more than 30 journal papers. He has participated in more than 20 European projects and received the IBM SUR award for his activities in WebSearch Similarity Search in Multimedia Data. Currently is Prof. Zezula is a primary investigator of the Czech Centre of Excellence on Large-Scale Multimodal Data Interpretation.
Prof. Pavel Zezula
Affiliation: Masaryk University, Faculty of Informatics, Brno, Czech Republic
Though searching is already the most frequently used application of information technology today, similarity approach to searching is increasingly playing more and more important role in construction of new search engines. In the last twenty years, the technology has matured and many centralized, distributed, and even peertopeer architectures have been proposed. However, the use of similarity searching in numerous potential applications is still a challenge. In the talk, four research directions in developing similarity search applications at Masaryk University DISA laboratory are to be discussed. First, we concentrate on accelerating largescale face recognition applications and continue with generic image annotation task for retrieval purposes. In the second half, we focus on data stream processing applications and finish the talk with the ambition topic of contentbased retrieval in human motioncapture data. Applications will be illustrated by online prototype implementations.