Conférences invitées

Foundations of Database Systems for Text Analytics

Benny Kimelfeld


Contemporary technological and social trends, such as mobile computing devices and social networking, result in an enormous amount of publicly available data with a high potential value within, known as “Big Data.” This phenomenon is complemented by modern business models, such as cloud computing and crowd sourcing, that provide a broad spectrum of consumers with the means to analyze the data. But that data have characteristics that introduce new challenges to data management systems; in particular, much of the data is free text in informal natural language. In this talk, I will discuss some fundamental topics in database systems tailored to text analytics, while focusing on my past work and visionary agenda; these topics include formal data and query models, related algorithmic problems, and the management of imprecision and uncertainty.


Benny Kimelfeld is an Associate Professor of the Computer Science Faculty at Technion - Israel Institute of Technology. After receiving his Ph.D. in Computer Science from The Hebrew University of Jerusalem, he has been a Research Staff Member at IBM Research – Almaden, and a Computer Scientist at LogicBlox. Benny’s research spans a spectrum of both foundational and systems aspects of data management, such as probabilistic databases, information retrieval over structured data, view updates, semistructured data, graph mining, and infrastructure for text analytics. Benny was an invited tutorial speaker at PODS 2014, a co-chair of the first SIGMOD/PODS workshop on Big Uncertain Data (BUDA), and currently serves as an associate editor in the Journal of Computer and System Sciences (JCSS).

Processing Data Streams

Toon Calders


Sometimes data is generated unboundedly and at such a fast pace that it is no longer possible to store the complete data in a database. The development of techniques for handling and processing such streams of data is very challenging as the streaming context imposes severe constraints on the computation:

  • We are often not able to store the whole data stream and making multiple passes over the data is no longer possible
  • As the stream is never finished we need to be able to continuously provide, upon request, up-to-date answers to analysis queries

Even problems that are highly trivial in an off-line context, such as: “How many different items are there in my database?“ become very hard in a streaming context.
Nevertheless, in the past decades several clever algorithms were developed to deal with streaming data. This talk covers several of these indispensable tools that should be present in every big data scientists’ toolbox.


Toon Calders graduated in 1999 from the University of Antwerp with a diploma in Mathematics. He received his PhD in Computer Science from the same university in May 2003, in the database research group ADReM, and continued working in the ADReM group as a postdoc until 2006. From 2006 till 2012 he was an assistant professor in the Information Systems group at the Eindhoven Technical University. In 2012 he joined the CoDE department at the ULB as a “Chargé de Cours” (associate professor). His main research interests include data mining and machine learning. Toon Calders published over 60 conference and journal papers in this research area and received several scientific awards for his works, including the recent “10 Year most influential paper” award for papers published in ECMLPKDD 2002. Toon Calders regularly serves in the program committees of important data mining conferences, including ACM SIGKDD, IEEE ICDM, ECMLPKDD, SIAM DM, was conference chair of the BNAIC 2009, EDM 2011, and ECML/PKDD 2014 conferences and is an editor for Springer Data Mining journal.

SAS Visual Analytics – A new approach towards BI

Nele Coghe, Pre-Sales Consultant at SAS


The BI and analytics platform market is undergoing a fundamental shift. Companies are increasingly shifting from using the installed base, traditional, IT-centric platforms that are the enterprise standard, to more decentralized data discovery deployments that are now spreading across the enterprise. The transition is to platforms that can be rapidly implemented and can be used by either analysts and business users, to find insights quickly, or by IT to quickly build analytics content to meet business requirements to deliver more timely business benefits.

Making analytics more accessible and pervasive to a broader range of users and use cases is the primary goal of organizations making this transition. Business users are looking for easier and faster ways to discover relevant patterns and insights in data.

SAS Visual Analytics (released in 2012) is the flagship product of SAS for delivering interactive, self-service analytic capabilities at an enterprise level — extending the reach of SAS beyond its traditional user base of power users, data scientists and IT developers within organizations. In this session the audience will get a live demo of the capabilities of SAS Visual Analytics.