Future Directions in Data Warehousing Research DOLAP ’04 Panel Discussion Karen C. Davis Electrical & Computer Engineering and Computer Science Dept. University of Cincinnati Cincinnati, OH USA

Perspectives Workshop: Data Warehousing at the Crossroads Schloss Dagstuhl International Conference and Research Center for Computer Science J. Hammer and M. Schneider (University of Florida) and T. Sellis (National Technical University of Athens) August 1-6, 2004Seminar 04321

Motivation volume of data increases at a staggering rate complexity of structure and semantics increases representation, manipulation and analysis for novel applications Goals review state-of-the-art discuss recent advances and trends identify interesting research problems

Areas for Working Groups design and modeling –conceptual modeling –requirements analysis –bridging the gap to data mining –security –metrics –evolution and versioning –interoperability –logical models –design methods architecture and processes

Conceptual Modeling state of the art: several models proposed for representing facts, ETL processes, use cases, and constraints challenges: unified, extensible model with formal semantics benefits: CASE tools; wide-applicability of research results

Quality Metrics state of the art: quality models in metadata; normal forms for DW schemas proposed challenges: defining metrics for measuring and maintaining system quality (both schema and data quality) benefits: better designs and better managed evolution

Evolution state of the art: schema evolution and versioning proposed in the literature challenges: providing effective versioning and data migration mechanisms to support queries over multiple versions; propagating changes to ETL processes benefits: avoids data warehouse obsolence and increases flexibility of queries and what-if analysis

Architecture