research-article

Snapshot semantics for temporal multiset relations

Authors:

Michael Böhlen,

Johann GamperAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 6

Pages 639 - 652

https://doi.org/10.14778/3311880.3311882

Published: 01 February 2019 Publication History

Abstract

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations.

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Cited By

Christodoulou GBouros PMamoulis N(2024)LIT: Lightning-fast In-memory Temporal IndexingProceedings of the ACM on Management of Data10.1145/36392752:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639275
Khnaisser CHamrouni HBlumenthal DDignös AGamper J(2024)Efficiently Labeling and Retrieving Temporal Anomalies in Relational DatabasesInformation Systems Frontiers10.1007/s10796-024-10495-wOnline publication date: 31-May-2024
https://doi.org/10.1007/s10796-024-10495-w
Krótkiewicz MMikłasz J(2024)A Structure Based on B+ Trees to Represent a Large Number of k-Multisets Stored in Non-volatile MemoryRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-97-5937-8_22(262-274)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-5937-8_22
Show More Cited By

Snapshot semantics for temporal multiset relations
1. Information systems
  1. Data management systems
    1. Database management system engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 6

February 2019

100 pages

ISSN:2150-8097

Editors:
Lei Chen
HKUST
,
Fatma Özcan
IBM Research - Almaden

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VLDB Endowment

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Published: 01 February 2019

Published in PVLDB Volume 12, Issue 6

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Cited By

Christodoulou GBouros PMamoulis N(2024)LIT: Lightning-fast In-memory Temporal IndexingProceedings of the ACM on Management of Data10.1145/36392752:1(1-27)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639275
Khnaisser CHamrouni HBlumenthal DDignös AGamper J(2024)Efficiently Labeling and Retrieving Temporal Anomalies in Relational DatabasesInformation Systems Frontiers10.1007/s10796-024-10495-wOnline publication date: 31-May-2024
https://doi.org/10.1007/s10796-024-10495-w
Krótkiewicz MMikłasz J(2024)A Structure Based on B+ Trees to Represent a Large Number of k-Multisets Stored in Non-volatile MemoryRecent Challenges in Intelligent Information and Database Systems10.1007/978-981-97-5937-8_22(262-274)Online publication date: 13-Aug-2024
https://doi.org/10.1007/978-981-97-5937-8_22
Reppas IMirabi MFathi LBinnig CDignös AGamper J(2024)Parallel Processing of Temporal Anti-Joins in MemoryDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_6(86-102)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5552-3_6
Ao JCheng ZChirkova RKolaitis P(2023)Theory and Practice of Relational-to-RDF Temporal Data Exchange and Query AnsweringJournal of Data and Information Quality10.1145/359135915:2(1-27)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3591359
Fowler SGalpin VCheney JScholz BKameyama Y(2022)Language-Integrated Query for Temporal DataProceedings of the 21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3564719.3568690(5-19)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1145/3564719.3568690
Grandi FMandreoli FMartoglia RPenzo W(2022)Unleashing the power of querying streaming data in a temporal database worldInformation Systems10.1016/j.is.2021.101872103:COnline publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.is.2021.101872
Gamper JCeccarello MDignös A(2022)What’s New in Temporal Databases?Advances in Databases and Information Systems10.1007/978-3-031-15740-0_5(45-58)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_5
Khnaisser CHamrouni HBlumenthal DDignös AGamper J(2022)Querying Temporal Anomalies in Healthcare Information Systems and BeyondAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_16(209-222)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_16
Dignös ABöhlen MGamper JJensen CMoser P(2021)Leveraging range joins for the computation of overlap joinsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-021-00692-331:1(75-99)Online publication date: 28-Aug-2021
https://dl.acm.org/doi/10.1007/s00778-021-00692-3
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