BDAS`05-12 paper searching system
Home > Description
Prof. Dr. Dirk Labudde
Bioinformatics group Mittweida (bigM) and Forensic Science Investigation Lab (FoSIL), University of Applied Sciences, Mittweida, Germany
Bio: Dirk Labudde is a professor at the University of Applied Sciences Mittweida, Germany, since September 1, 2009. He received his diploma in 1993 and obtained his Ph.D. in theoretical physics in 1997, both at Rostock University, while also studying medical physics at Kaiserslautern University. He later worked as a lecturer and research assistant at Medical School and Clinical Center for Neurosurgery in Neubrandenburg, Leibnitz Institute for Molecular Pharmacology in Berlin, Technical University Munich, and Technical University Dresden before accepting a professorship position for bioinformatics and forensics at Mittweida.
His main areas of research are algorithms and computational methods in (digital) forensics and structural bioinformatics.
Title: to be announced
Prof. Jean-Charles Lamirel
SYNALP team, LORIA, Vandœuvre-lès-Nancy, France
Bio: Jean-Charles Lamirel is a lecturer since 1997. He got his PhD in 1995 and his Research Accreditation in 2010. He is currently teaching Information Science and Computer Science at the University of Strasbourg and achieving his research at the INRIA-LORIA laboratory of Nancy (France). He was a research member of the INRIA-CORTEX project whose scope is Neural Networks and Biological Systems. He has then integrated the INRIA-TALARIS project (recently becoming the LORIA SYNALP Project) whose main concern is automatic language and text processing. His main domain of research is Textual Data Mining based on Neural Networks. He has interests in both theoretical models for Data Mining and Data Mining applications. He is more specifically specialized in unsupervised learning methods. He is the creator of the paradigms of Data Analysis based on Multiple Viewpoints (MVDA) and Measure based on Feature Maximization (F-Max). The related models for which it has been proven that they outperform classical models begin to be used in many challenging Data Mining applications. His other main topics of research concern Visualization Methods for Data Analysis, Quality Issues in Data Analysis and Novelty Detection models. He and his tools have been currently involved in several European projects on Webometrics and Data Analysis, like the recent EISCTES project. He is a board member of international Webometrics journals and organizer of international conferences in the same domain. His research work and direction led to the successful presentation of more than 10 different Ph.Ds. thesis. It also generated an important scientific production: more than 20 invited conferences, more than 10 special sessions in international conferences and more than 150 publications in international conferences and journals. This work also was worthy for him as a whole the recognition of many prestigious foreign institutional partners like NIEHS (USA), NSC (Taiwan), KU Leuwen (Belgium), UTS (Australia) and NISTAD (India).
Title: to be announced
Prof. Witold Pedrycz
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Bio: Witold Pedrycz is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He also holds an appointment of special professorship in the School of Computer Science, University of Nottingham, UK. In 2009 Dr. Pedrycz was elected a foreign member of the Polish Academy of Sciences. In 2012 he was elected a Fellow of the Royal Society of Canada. Witold Pedrycz has been a member of numerous program committees of IEEE conferences in the area of fuzzy sets and neurocomputing. In 2007 he received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Council. He is a recipient of the IEEE Canada Computer Engineering Medal 2008. In 2009 he has received a Cajastur Prize for Soft Computing from the European Centre for Soft Computing for "pioneering and multifaceted contributions to Granular Computing". In 2013 has was awarded a Killam Prize. In the same year he received a Fuzzy Pioneer Award 2013 from the IEEE Computational Intelligence Society.
His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and Software Engineering. He has published numerous papers in this area. He is also an author of 15 research monographs covering various aspects of Computational Intelligence, data mining, and Software Engineering.
Dr. Pedrycz is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences and Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley). He currently serves as an Associate Editor of IEEE Transactions on Fuzzy Systems and is a member of a number of editorial boards of other international journals.
Granular Computing – Describing the World with Information Granules
Prof. Abdelkader Hameurlain
Pyramid Team, Institut de Recherche en Informatique de Toulouse IRIT, Paul Sabatier University, Toulouse Cedex, France
Bio: Abdelkader Hameurlain is full professor in Computer Science at Paul Sabatier University, Toulouse, France. He is a member of the Institute of Research in Computer Science of Toulouse (IRIT). His current research interests are in query processing and optimization in parallel and large-scale distributed environments, mobile databases, and database performance. Prof. Hameurlain has been the general chair of the International Conference on Database and Expert Systems Applications (DEXA`02 and DEXA`2011). He is co-editor in Chief of the International Journal "Transactions on Large-scale Data and Knowledge Centered Systems" (LNCS, Springer). He was guest editor of two special issues of "International Journal of Computer Systems Science and Engineering on "Mobile Databases" and "Data Management in Grid and P2P Systems".
Evolution of Data Management Systems: from Uniprocessor File Systems to Cloud Systems
Global and synthetic overview (since 1950 ===> until-today):
The purpose of this tutorial is to provide a comprehensive state of the art concerning the evolution of data management systems from uniprocessor file systems to cloud systems. In the landscape of database management systems, data analysis systems (OLAP) and transaction processing systems (OLTP) are separately managed. The reasons for this dichotomy are that both systems have very different functionalities, characteristics and requirements. The tutorial will focus on the first class OLAP systems. In this perspective, firstly, I introduce the main problems of data management systems DMS. Then, for each environment (e.g. uniprocessor, parallel, distributed; large-scale), I describe synthetically, the underlying concepts and the main characteristics of the proposed DMS. I also explain the relationships between those DMS. Lastly, I provide a classification and a qualitative comparison of main cloud DMS. These systems are compared, by relying on fundamental criteria (e.g. software requirements, scalability, elasticity), to parallel relational DBMS. I point out their advantages and weaknesses, and the reasons for which the relevant choice of a data cloud system is very hard.
Outline of the Tutorial
I. Introduction: Main Problems of Data Management Systems DMS
II. Evolution of Data Management Systems: From Uniprocessor File Systems to Large-scale DMS (through Parallel and Distributed DB Systems)
-- Motivations and Objectives
-- Underlying Concepts
-- Main Characteristics of Proposed DMS
-- Relationships between DMS
III. Towards Cloud Data Management Systems
-- Motivations & Objectives
-- Cloud Architectures and Main Characteristics
-- Classification and Maturity of Cloud Data Management Systems
-- Parallel Relational DBMS versus Cloud Data Management Systems
IV. Conclusion & References
Requirements for attendees (Skills): Data Management, Relational DB Systems, Parallel and Distributed DB Systems, Scalability, Elasticity