Cost parameters and database statistics are the basis of query optimization techniques. However, in distributed and heterogeneous database systems, acquiring and treating information in order to help the optimization process are often tasks of a global query processor, which adapts its functionalities to a specific system architecture. Moreover, this acquisition process involves a large number of parameters and requires customized methods to retrieve data from specific sources. DIG (Distributed Information Gatherer) is a provider of data statistics and query costs that, through an independent and flexible service, aims to support global query optimization processing in distributed and heterogeneous database systems over autonomous data sources. We have developed a DIG prototype and experimented it with specific wrappers for a query middleware on both semi-structured data sources and an object DBMS. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Ruberg, N., Ruberg, G., & Mattoso, M. (2003). Digging database statistics and costs parameters for distributed query processing. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2888, 301–318. https://doi.org/10.1007/978-3-540-39964-3_20
Mendeley helps you to discover research relevant for your work.