Pr. Ladjel BELLATRECHE
Poitiers University - France
Pr. Ladjel BELLATRECHE is a full Professor at National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA), Poitiers, France, where he joined as a faculty member since Sept. 2010. He leads the Data and Model Engineering Team of the Laboratory of Computer Science and Automatic Control for Systems (LIAS). Prior to that, he spent eight years as Assistant and then Associate Professor at Poitiers University, France. He was a Visiting Professor of the Québec en Outaouais, Canada, a Visiting Researcher at the Department of Computer Science, Purdue University, USA and the Department of Computer Science of Hong Kong University of Science and Technology, China (1997-1999). His research interest focuses on Data and Model Management. He has co-authored more than 250 papers and received > 2742 citations (H-index=26). He serves as an Associate Editor of the Data & Knowledge (DKE) Journal, Elsevier, an Editorial Board of International Journal of Reasoning-based Intelligent Systems, Inderscience, Scalable Computing Journal, Springer and Computer Science and Information Systems Journal. He organized/co-organized numerous international and French Conferences and Workshops (ADBIS, DAWAK, ACM DOLAP, MEDI, IEEE SCC, IEEE Smart Data, WISE, EDA, BDA). He has acted as an evaluator for funding agencies in Algeria, France, EU, Czech Republic, Kazakhstan, and the Netherlands. He actively contributes to promoting research in Africa and Asia, where he co-supervised several students. He co-founds several conferences and workshops (MEDI, CIIA, OAIS).
Talk 2: Data Cube is Dead, Long Life to Data Cube in the Age of Web Data
In a short time, the data warehouse (DW) technology took an important place in the academic and industrial landscapes. This place materialized in the large majority of engineering and management schools that adopted it in their curriculum and in the small, medium-size and large companies that enhanced their decision-making capabilities thanks to it. The 1990s saw the advent of conferences such as DaWaK and DOLAP that carried the acronyms DW and OLAP in their titles. Then, all of a sudden, this technology has been upset by the arrival of Big Data. Consequently, those schools, companies and research communities have replaced DW and OLAP by Big Data Analytics. We are well placed to assert that this brutal move may have a negative impact on schools, academia, and industry. This technology is not dead, today's context, with the connected world and Web of Data, is more favorable than when building DW merely stemmed from company internal sources. In this invited paper, we attempt to answer the following question: how does DW technology interact with Linked Open Data (LOD)? To answer the question, we provide a complete vision to augment the traditional DW with LOD, to capture and quantify the added value generated through this interaction. This vision covers the main steps of the DW life-cycle. This value is estimated though two different perspectives: (i) a source-oriented vision, by calculating the rate of the DW augmentation in terms of multidimensional concepts and instances, and (ii) a goal-oriented vision where the value is calculated according to the ability of the DW to estimate the performance levels of defined goals that reflect the strategy of a company, using the defined DW of the case study of a leading Algerian company.