SUBJECT AREA DESCRIPTION AND MOTIVATION

Nowadays, data warehouses are worldwide accepted and obligatory components of decision support systems. From a technological point of view, a data warehouse is a database that integrates multiple, usually heterogeneous, distributed, and autonomous data sources. An inherent feature of data sources is that they can evolve in time independently of a DW that integrates them. The evolution of EDSs can be characterized by content changes, i.e., insert/update/delete data, and schema changes, i.e., add/modify/drop a data structure or its property. The propagation of content changes into a DW is handled by means of materialized views. The evolution of DWs caused by the propagation of schema changes is much more difficult to handle and has not been fully solved yet.

The evolution of data sources has also impact on the Extraction-Translation-Loading (ETL) software used for feeding a data warehouse with data. For these reasons, it is inevitable to develop solutions for managing the evolution of data warehouses and ETL software.

The just emerging DW technologies applied to complex data like XML, spatio-temporal, and multimedia data will also suffer from structural and content changes in data sources and will face the same problems as traditional data warehouses. Moreover, currently, semantic Web, knowledge bases, and reasoning technologies are being incorporated into decision support systems. They will soon become technologies complementary to DW and OLAP, aiming at yet better decision support. These new technologies heavily use ontologies. Ontologies will evolve for similar reasons as information systems evolve. Thus, research in this field will face problems with managing the evolution of ontologies.

AIM

The aim of this workshop is twofold. Firstly, to gather those researchers and industry developers who focus on handling dynamics in data warehouses, in order to discuss their achievements and open issues. Secondly, to inspire a broader audience to further research and development in the area.

MAJOR TOPICS OF INTEREST

• Data warehouse architectures for evolution support
• Data warehouse modeling for evolution support
• Temporal and multiversion data warehouses
• Managing the evolution of ETL
• Query languages and OLAP tools for evolving data warehouses
• Integrity constraints for evolving data structures
• Indexing temporal and multiversion data
• Metadata management and querying
• Temporal and evolving ontologies
• Quality of evolving data
• Case studies, prototype systems, experience reports
• Surveys on research approaches, prototypes, and commercial systems

PROGRAM COMMITTEE

• Ladjel Bellatreche, Ecole Nationale Supérieure de Mécanique et d'Aérotechnique, France
• Alfredo Cuzzocrea, ICAR-CNR and University of Calabria, Italy
• Matteo Golfarelli, University of Bologna, Italy
• Marcin Gorawski, Silesian University of Technology, Poland
• Carlos Hurtado, Universidad de Chile, Chile
• Stanislaw Kozielski, Silesian University of Technology, Poland
• Tadeusz Morzy, Poznan University of Technology, Poland
• Stefano Rizzi, University of Bologna, Italy
• Alkis Simitsis, HP Labs, Palo Alto, USA
• Alejandro Vaisman, Universidad de Buenos Aires, Argentina
• Panos Vassiliadis, University of Ioannina, Grece