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Keynote Lectures

Main-Memory Centric Data Management – Open Problems and Some Solutions
Wolfgang Lehner, Technische Universität Dresden, Germany

Context-Aware Decision Support in Dynamic Environments - Theoretical & Technological Foundations
Alexander Smirnov, SPIIRAS, Russian Federation

 

Main-Memory Centric Data Management – Open Problems and Some Solutions

Wolfgang Lehner
Technische Universität Dresden
Germany
 

Brief Bio
Prof. Dr.-Ing. Wolfgang Lehner is full professor and head of the database systems group at Technische Universität Dresden (TU Dresden), Germany. He received his Master’s degree in Computer Science in 1995 from University of Erlangen-Nuremberg. He continued his studies as research assistant at the database system group until 1998, when he earned his Ph.D. degree (Dr.-Ing.) with a dissertation on optimization of aggregate processing in multidimensional database systems. In 7/2001 he finished his habilitation with a thesis on subscription systems and was therefore awarded with the venia legendi. Wolfgang Lehner conducts are variety of different research projects with his team members ranging from designing data-warehouse infrastructures from a modeling perspective, supporting data-intensive applications and processes in large distributed information systems, adding novel database functionality to relational database engines to support data mining/forecast algorithms, investigating techniques of approximate query processing (e.g. sampling) to speed up execution times over very large data sets, and exploiting the power of main-memory centric database architectures with an emphasis on modern hardware capabilities. Apart from basic and mostly theoretic research questions (supported by Technische Universität Dresden and the German Research Foundation, DFG) Prof. Lehner puts a strong emphasis on practical research work in combination with industrial cooperations, on an international, national, and regional level. Since 2013, Wolfgang Lehner leads the the DFG Research Training Group "Role-oriented Software Infrastructures" (RoSI), which develops new techniques for context-adaptive software, from language and application design to run time (rosi-project.org).


Abstract

Data management systems are currently sandwiched by two major developments. On the one hand, the underlying hardware characteristics has changed dramatically within the last years by providing a huge number of cores and extremely large main memory capacities in the Terabyte range for commodity servers. While these developments have great impact on system architecture, current systems are only slowly starting to exploit these capabilities. On the other hand, more and more non-standard applications are eager to take advantage of the features provided by database management systems. Especially knowledge extraction processes interactively analyzing large, mostly empirically collected datasets are generating a huge variety of requirements to the underlying data management platform using complex statistical models. Within the keynote, I will dive into some detail with respect to different requirements giving examples from different areas. From there I will derive open problems and give some solutions to pave the way for positioning database systems as the central information hub for operational applications and analytical knowledge extraction processes.



 

 

Context-Aware Decision Support in Dynamic Environments - Theoretical & Technological Foundations

Alexander Smirnov
SPIIRAS
Russian Federation
 

Brief Bio
Alexander V. Smirnov is head of Computer Aided Integrated Systems Laboratory at St.Peterburg Institute for Informatics and Automation of the Russian Academy of Sciences - SPIIRAS (1994), Deputy-Director for Research (1996). He received his Ph.D from St.Petersburg State University of Electrical Engineering (1984) and D.Sc. from SPIIRAS (1994), and became a Full Professor in 1998. Currently he is a part-time full professor of St.Petersburg State Electrical Engineering University, Department of Research Automation. He has been involved in projects sponsored by Ford, Nokia, US DoD, European Research Programs (Information Society Technologies, Esprit, Eureka/Factory, etc.), and Russian agencies in the areas of distributed intelligent systems, ontology management, intelligent decision support systems, etc. He is a member of technical committee of IFAC TC 5.1 on Manufacturing Plant Control; IEEE SMC TC on Self-Organized Distributed and Pervasive Systems, IFIP TC WG5.1 on Global Product Development for the Whole Life-Cycle. His current research is in the areas of ontology-driven information integration, context management, operational decision support, virtual organization management. He published more than 300 research papers in reviewed journals and proceedings of international conferences, books, manuals.


Abstract
Context-aware decision support is required in situations happening in dynamic, rapidly changing, and often unpredictable distributed environments. Such situations can be characterized by highly decentralized up-to-date data sets coming from various resources located in cyber-physical space. The goals of context-aware support of operational decision making are to timely provide the decisions maker with up-to-date information, to assess the relevance of information & knowledge to a decision, and to gain insight in seeking and evaluating possible decision alternatives.
The lecture addresses theoretical and technological foundations of context-aware decision support. The theoretical fundamentals are built around ontologies as a widely accepted tool for the semantic modeling of context information. They provide efficient facilities to represent application knowledge, and to make resources of the dynamic environments context-aware and interoperable.
The proposed fundamentals are supported by advanced intelligent technologies (ontology management, context management, constraint satisfaction, smart space, and decision mining). An application of these ideas is illustrated by examples of decision support systems for dynamic logistics.



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