In this paper we explore database segmentation in the context of a column-store DBMS targeted at a scientific database. We present a novel hardware- and scheme-oblivious segmentation algorithm, which learns and adapts to the workload immediately. The approach taken is to capitalize on (intermediate) query results, such that future queries benefit from a more appropriate data layout. The algorithm is implemented as an extension of a complete DBMS and evaluated against a real-life workload. It demonstrates significant performance gains without DBA assistance.

Cracking a Scientific Database
IEEE International Conference on Data Engineering
Database Architectures

Ivanova, M., Kersten, M., & Nes, N. (2008). Adaptive Segmentation for Scientific Databases.