Flower Master Index for Relational Database Selection and Joining

Kvet, Michal

doi:10.1007/978-3-030-63872-6_9

Towards Digital Intelligence Society

2020

DOI: 10.1007/978-3-030-63872-6_9

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Flower Master Index for Relational Database Selection and Joining

Michal Kvet

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2021

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Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

et al. 2021

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HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

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Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

et al. 2021

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Flower Master Index for Relational Database Selection and Joining

Cited by 1 publication

References 22 publications

Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

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