Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7287
Authors: Bouros, Panagiotis
Mamoulis, Nikos
Tsitsigkos, Dimitrios
Terrovitis, Manolis
Title: In-Memory Interval Joins
Online publication date: 4-Jul-2022
Language: english
Abstract: The interval join is a popular operation in temporal, spatial, and uncertain databases. The majority of interval join algorithms assume that input data reside on disk and so, their focus is to minimize the I/O accesses. Recently, an in-memory approach based on plane sweep (PS) for modern hardware was proposed which greatly outperforms previous work. However, this approach relies on a complex data structure and its parallelization has not been adequately studied. In this article, we investigate in-memory interval joins in two directions. First, we explore the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation. We propose four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art. Second, we study in depth the parallel computation of interval joins. We design a non-partitioning-based approach that determines independent tasks of the join algorithm to run in parallel. Then, we address the drawbacks of the previously proposed hash-based partitioning and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach, we propose a novel breakdown of the partition-joins into mini-joins to be scheduled in the available CPU threads and propose an adaptive domain partitioning, aiming at load balancing. We also investigate how the partitioning phase can benefit from modern parallel hardware. Our thorough experimental analysis demonstrates the advantage of our novel partitioning-based approach for parallel computation.
DDC: 004 Informatik
004 Data processing
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 08 Physik, Mathematik u. Informatik
Place: Mainz
ROR: https://ror.org/023b0x485
DOI: http://doi.org/10.25358/openscience-7287
Version: Published version
Publication type: Zeitschriftenaufsatz
License: CC BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: The VLDB Journal
30
Pages or article number: 667
691
Publisher: Springer
Publisher place: Berlin u.a.
Issue date: 2021
ISSN: 0949-877X
Publisher DOI: 10.1007/s00778-020-00639-0
Appears in collections:JGU-Publikationen

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