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• W2562053008 abstract "Subgraph queries are central to graph analytics and graphDBs. We analyze this problem and present key novel discoveriesand observations on the nature of the problem whichhold across query sizes, datasets, and top-performing algorithms.Firstly, we show that algorithms (for both the decisionand matching versions of the problem) suffer fromstraggler queries, which dominate query workload times. Asrelated research caps query times not reporting results forqueries exceeding the cap, this can lead to erroneous conclusionsof the methods’ relative performance. Secondly, westudy and show the dramatic effect that isomorphic graphqueries can have on query times. Thirdly, we show thatfor each query, isomorphic queries based on proposed queryrewritings can introduce large performance benefits. Fourthly,that straggler queries are largely algorithm-specific: manychallenging queries to one algorithm can be executed effi-ciently by another. Finally, the above discoveries naturallylead to the derivation of a novel framework for subgraphquery processing. The central idea is to employ parallelismin a novel way, whereby parallel matching/decision attemptsare initiated, each using a query rewriting and/or an alternatealgorithm. The framework is shown to be highly beneficialacross algorithms and datasets." @default.
• W2562053008 created "2017-01-06" @default.
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• W2562053008 date "2017-03-21" @default.
• W2562053008 modified "2023-09-26" @default.
• W2562053008 title "Subgraph Querying with Parallel Use of Query Rewritings and Alternative Algorithms." @default.
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• W2562053008 doi "https://doi.org/10.5441/002/edbt.2017.04" @default.
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