Conférenciers invités

Yannick Cras

Yannick Cras, SAP Business Object, France

Yannick Cras is Chief Development Architect, Core BI technology at SAP BusinessObjects. As such he provides individual expertise, leadership and executive influence in the design of the core Business Intelligence technology of the company, of which he authored a number of foundation patents such as the mathematical calculation model that underlies SAP’s flagship BI product, WebIntelligence. Yannick’s technology interests are centered around data, calculation and query semantics, with a strong focus on devising creative solutions that merge expressive power and semantic soundness with user-friendliness in a class of products designed for non IT personnel. Yannick has been working for Business Objects, now SAP BusinessObjects, since 1995 in various technical management or leadership positions. Prior to that he worked for EDS Management Consulting Services as a Senior Consultant in the field of advanced combinatorial problem solving and Operations Research, where he devised complex optimization algorithms for clients such as Elf Atochem, Air France or SNCM. His career had started with Bull’s Artificial Intelligence Development group where he co-authored and co-developed the first industrial constraint programming language Charme.
Yannick holds an Engineer-Doctor degree in Computer Sciences from University Paris XI, an Engineering degree (option Applied Mathematics) from the Ecole Centrale Paris, and an Advanced Degree in Computer Sciences from University Paris VI. He is a member of the steering committee for the Business Objects BI Chair at ECP,
where he regularly gives talks. Yannick is also an amateur composer, poet and songwriter.

Title: Talking to your inner liberal arts major

Summary of the talk: One of the key challenges of Business Intelligence is to offer business users the opportunity of asking business questions in an intuitive manner, and receiving answers they can understand and trust. The problem is not about computing the right answers for a given query: it’s to interpret – and if necessary guide- the user’s actions in order to deduce their intent and make sure we answer the question they intended to ask in the first place.
As engineers, scientists and developers, we tend to adopt authoring metaphors which we find intellectually elegant but will puzzle most users. Subqueries, for instance, are simply out of reach of most users, and can be replaced by incremental construction of query elements.
Moreover, the lack of semantic depth in many tools – Excel to start with – can encourage users to perform mathematically expressible yet generally meaningless computations, such as an average of percentages. More generally, the subtlety of business questions is largely ignored, and BI tools tend to make a priori, undocumented decisions about the intended semantics of business questions – leading not to wrong, but to misleading answers.
Through the exploration of a number of real-life examples, this presentation aims at identifying how we can at last adapt our query metaphors to a general audience.

Alexander Löser

Alexander Löser, Technische Universität Berlin, Germany

Alexander Löser leads a research group at the Technische Universität Berlin in the department of data base systems and information management. Previously, he worked as a senior research scientist and project manager at HP Labs Bristol, for the IBM Almaden Research Center, and for SAP AG. His research interests are in the area of Business Intelligence, in particular in methods for Web-scale text-analytics and in pricing strategies for information marketplaces.

Alexander has published over 30 refereed scientific papers in prestigious international conferences and journals, held more than 40 invited lectures at industrial companies, conferences, universities and holds several patents. His research has been incorporated into the commercial product IBM Lotus Notes. Alexander is a regular program committee member of the world’s leading data mining and business intelligence venues. Learn more about Alexander at

Title: Information Marketplaces for Big Data Analytics

The analysis of freely available Big Data is an increasing market segment. Currently, multiple data vendors utilize the cloud-computing paradigm for trading data, associated analytical data services, and analytic results as a commodity good on information marketplaces. In the first part of this talk we present insights from interviews with established vendors about typical queries and key challenges with regard to pricing strategies in different market situations.

In the second part of the talk I introduce to the technical infrastructure of the MIA marketplace. This large research project will provide an infrastructure which ensures the sustainable operation of a reliable and trusted platform for the production, provision and use of the data of the .DE Web and other free information. This infrastructure enables completely new business models with information and analysis as electronically tradable goods. The collective storage, analysis and utilization of data from the .DE-Web offers many cost savings and high innovation capabilities. Thus, especially for small and medium enterprises, significant market entry barriers and impediments to innovation are eliminated. The talk concludes with interesting research problems for the business intelligence, database and text mining communities.

Panos Vassiliadis

Panos Vassiliadis, University of Ioannina, Greece

Panos Vassiliadis graduated the Varvakio Experimental School in 1990 and obtained his Diploma in Electrical Engineering and his PhD from the Department of Electrical and Computer Engineering, of the National Technical University of Athens (NTUA) in 1995 and 2000, respectively. He has joined the Department of Computer Science of the University of Ioannina in 2002 and since then, He is a member of the Distributed Management of Data (DMOD) Laboratory. So far, his research has focused on Data Warehouse technology, with particular interest on issues like data warehouse metadata repositories and metadata modeling, data warehouse quality, On-Line Analytical Processing (OLAP), and Extraction-Transformation-Loading (ETL). Currently, his on-going research is also targeted towards Metadata-Rich Data-Centric Information Systems, with particular emphasis to the modeling, pattern-based design, and evolution of their underlying database infrastructure as well as to Web-Services with particular emphasis to SOA maintenance.

Title: Jenga and the art of data-intensive ecosystems maintenance

Software maintenance amounts up to 60% of the resources spent on building and operating a software system. Data-intensive ecosystems that include several software applications tightly coupled to underlying data repositories cannot escape the above rule. In such environments, the impact of evolution is three-fold: (a) syntactical (meaning, that the evolution of either the data repositories or the software that implements the operational processes of the data warehouse can lead to operational failures and crashes due to some form of syntactic incorrectness), (b) semantic (meaning that changes in a view or a data transformation module can lead to different semantics for the propagated data), and (c) performance-oriented, as different configurations of the ecosystem’s components (be it data or software) lead to different performance for its operations.
As in all data-intensive ecosystems, the evolution of the software constructs of a data warehouse environment can severely affect its operations. In this talk, we focus on two aspects of the management of data warehouse evolution. On the one hand, we are interested in predicting the maintenance effort of ETL workflows. To this end, we present the findings of a case study on how a set of graph-theoretic metrics can be used for the prediction of evolution vulnerability for the components of ETL scenarios. On the other hand, we are interested in supporting the graceful evolution of the ecosystem’s components and we present a method for the adaptation of ecosystems that assess the potential impact of a change and rewrite the ecosystem’s components in order to adapt to the change.