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AutoPart: Automating Schema Design for Large Scientific Databases Using Data Partitioning
 

Summary: AutoPart: Automating Schema Design for Large Scientific
Databases Using Data Partitioning
Stratos Papadomanolakis Anastassia Ailamaki
Carnegie Mellon University Carnegie Mellon University
stratos@cs.cmu.edu natassa@cmu.edu
Abstract
Database applications that use multi-terabyte datasets are
becoming increasingly important for scientific fields such as
astronomy and biology. Scientific databases are particularly
suited for the application of automated physical design tech-
niques, because of their data volume and the complexity of the
scientific workloads. Current automated physical design tools
focus on the selection of indexes and materialized views. In
large-scale scientific databases, however, the data volume and
the continuous insertion of new data allows for only limited
indexes and materialized views. By contrast, data partitioning
does not replicate data, thereby reducing space requirements and
minimizing update overhead. In this paper we present AutoPart,
an algorithm that automatically partitions database tables to
optimize sequential access assuming prior knowledge of a repre-

  

Source: Ailamaki, Anastassia - School of Computer Science, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences