Efficient and Error-bounded Spatiotemporal Quantile Monitoring in Edge Computing Environments

Huan Li, Lanjing Yi, Bo Tang, Hua Lu, Christian S. Jensen

Research output: Contribution to journalConference article in JournalResearchpeer-review

2 Citations (Scopus)
32 Downloads (Pure)

Abstract

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtu-alizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The coordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM.

Original languageEnglish
JournalProceedings of the VLDB Endowment
Volume15
Issue number9
Pages (from-to)1753-1765
Number of pages13
ISSN2150-8097
DOIs
Publication statusPublished - 2022
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sept 20229 Sept 2022

Conference

Conference48th International Conference on Very Large Data Bases, VLDB 2022
Country/TerritoryAustralia
CitySydney
Period05/09/202209/09/2022

Bibliographical note

Publisher Copyright:
© 2022, VLDB Endowment.

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