General dynamic Yannakakis: conjunctive queries with theta joins under updates

The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. In prior work, we have proposed general dynamic Yannakakis ( GDyn ), a general framework for dynamically processing acyclic conjunctive queries with θ -joins in the presence of data update...

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Vydané v:The VLDB journal Ročník 29; číslo 2-3; s. 619 - 653
Hlavní autori: Idris, Muhammad, Ugarte, Martín, Vansummeren, Stijn, Voigt, Hannes, Lehner, Wolfgang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020
Springer Nature B.V
Predmet:
Algorithms
Computer Science
Data structures
Database Management
Queries
Query processing
Special Issue Paper
Supports
Tradeoffs
Incremental view maintenance
Theta joins
Acyclic joins
Dynamic query processing
Complex event processing
Inequalities
ISSN:1066-8888, 0949-877X
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Shrnutí:The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. In prior work, we have proposed general dynamic Yannakakis ( GDyn ), a general framework for dynamically processing acyclic conjunctive queries with θ -joins in the presence of data updates. Whereas traditional approaches face a trade-off between materialization of subresults (to avoid inefficient recomputation) and recomputation of subresults (to avoid the potentially large space overhead of materialization), GDyn is able to avoid this trade-off. It intelligently maintains a succinct data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In this paper, we consolidate and extend the development of GDyn . First, we give full formal proof of GDyn ’s correctness and complexity. Second, we present a novel algorithm for computing GDyn query plans. Finally, we instantiate GDyn to the case where all θ -joins are inequalities and present extended experimental comparison against state-of-the-art engines. Our approach performs consistently better than the competitor systems with multiple orders of magnitude improvements in both time and memory consumption.
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ISSN:1066-8888
0949-877X
DOI:10.1007/s00778-019-00590-9