SHARq
| Autor: | Mirela T. Cazzolato, Lucas C. Scabora, Jose F. Rodrigues-Jr, Gabriel Spadon, Caetano Traina-Jr, Agma J. M. Traina, Daniel S. Kaster |
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| Rok vydání: | 2021 |
| Předmět: |
Recursive join
Connected component Power graph analysis Sequence Theoretical computer science Computer science Relational database InformationSystems_DATABASEMANAGEMENT 020207 software engineering 02 engineering and technology computer.software_genre law.invention Set (abstract data type) Relational database management system PageRank law 020204 information systems 0202 electrical engineering electronic engineering information engineering computer |
| Zdroj: | SAC |
| Popis: | Processing navigational graph-like queries in relational databases requires executing several recursive join operations, which are computationally costly. However, when the need for graph-like queries arises, applications often execute a sequence of related queries in a single session. We argue that it is possible to reduce the total cost of a set of related queries, by expanding individual intermediate results and sharing them among multiple queries. SHARq is our framework that enables sharing intermediate results of the common graph-like queries Single-Source Shortest Paths (SSSP), Connected Components (CC), and PageRank (PR). Our solution prepares result tables expanded with additional columns to store partial results of graph-like query combinations, such as multiple SSSP, or a sequence of queries comprising SSSP, CC, and PR. Experimental results on 9 datasets show query speedups of up to ten times when combining multiple SSSP queries, and up to two times when combining SSSP, CC, and PR queries. The results reveal a significant reduction in the query time, providing timely results for analyses relying on multiple navigational graph-like queries. |
| Databáze: | OpenAIRE |
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