Cardinality estimation for the optimization of queries on ontologies

Abstract

An effective, accurate algorithm for cardinality estimation of queries on ontology models of data is presented. The algorithm relies on the decomposition of queries into query pattern paths, where each path produces a set of values for each variable within the result form of the query. In order to estimate the total number of result set parameters for each path, a set of statistics is compiled on the properties of the ontology. Experimental analysis has shown that the algorithm produces estimates with high accuracy and with high correlation to actual values. Thus, this algorithm can be used as the cornerstone of an effective optimization strategy for queries on diverse, heterogeneous data sources modeled as ontologies.

semQA: SPARQL with Idempotent Disjunction

“…The ability to devise multiple query plans by algebraic manipulation and filter pushdown is necessary in order to design such query optimization strategies. The design of such strategies, including the use of cardinality estimation metrics such as those proposed in [17], is outside the scope of this paper and a matter for future work.…”

“…This provides a powerful tool for the optimization of queries, in combination with strategies to estimate the cardinality of partial results such as those proposed in [17]. The results obtained by this approach, however, are nonconformant to the current status of the SPARQL standard.…”

Fine-Grained Diagnostics of Ontologies with Assurance

“…As an example a maximal complete query pattern path consists of a maximal query pattern path and a set of value constraints (cf. Shironoshita et al (2007)). The total cardinality of such a path is obtained by calculating the product of the cardinality estimate of its maximal query path with all the value ratios for every variable in the query pattern path (cf.…”

“…The total cardinality of such a path is obtained by calculating the product of the cardinality estimate of its maximal query path with all the value ratios for every variable in the query pattern path (cf. Shironoshita et al (2007)). In a highly distributed architecture where data in different locations connected through the internet, this is the most critical aspect of query execution time and the speed of the connections and the amount of data plays an important role.…”

SPARQL Query Optimization on Top of DHTs

“…This can be a very expensive operation in a distributed setting such as a DHT. Finally, a method for the cardinality estimation of SPARQL queries using a probability distribution is presented in [21].…”