Management of multidimensional aggregates for efficient online analytical processing

Abstract

Proper management of multidimensional aggregates is a fundamental prerequisite for efficient OLAP. The experimental OLAP server CUBESTAR, which concepts are described in this paper, was designed exactly for that purpose. All logical query processing is based solely on a specific algebra for multidimensional data. However, a relational database system is used for the physical storage of the data. Therefore, in popular terms CUBESTAR can be classified as a ROLAP system. In comparison to commercially available systems, CUBESTAR is superior in two aspects: First, the implemented multidimensional data model allows more adequate modeling of hierarchical dimensions, because properties which apply only to certain dimensional elements can be modeled context-sensitively. This fact is reflected by an extended star schema on the relational side. Second, CUBESTAR supports multidimensional query optimization by caching multidimensional aggregates. Since summary tables are not created in advance but as needed, hot spots can be adequately represented. The dynamic and partition-oriented caching method allows cost reductions of up to 60% with space requirements of less than 10% of the size of the fact table.

Mediating Open Data Consumption - Identifying Story Patterns for Linked Open Statistical Data

“…Conventional OLAP data model approach does not differentiate information about classifiers and characterizers. In [1] [29] propose an "SQLm" -Multidimensional SQL language, which provides compatibility with legacy databases. is high-level, user-oriented concept performs automatic aggregations and supports irregular dimension hierarchies.…”

Usability-based caching of query results in OLAP systems

“…Recently, query result caching approaches for DWs and OLAP systems have been proposed which select query results to be stored in the cache dynamically (Scheuermann et al, 1996;Shim et al, 1999;Deshpande et al, 1998;Kortidis and Roussopoulos, 1999;Albrecht et al, 1999;Roy et al, 2000;Kalnis and Papadias, 2001). These approaches presented some goodness measures to choose previous query results to replace from the cache in order to adapt the cache contents to the changing workload.…”

A hybrid model for data synchronism in data warehouse projects

“…In situation (1), where the sets are disjoined, the synchronism model to be implemented in X and X' is the one defined by function , Y and Y', respectively.…”

“…Dynamic aggregations calculated at the time the query is performed [1]; Development of views with the required aggregation functions. They are called "aggregation views" and are used and computed during the query execution [2]; Aggregation views are stored in caches and used when answering later queries [3]; Summarization Tables representing aggregation views implemented as materialized views [4] [5]; Results from queries on the data warehouse.…”