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.
Originalsprog | Engelsk |
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Tidsskrift | Proceedings of the VLDB Endowment |
Vol/bind | 15 |
Udgave nummer | 9 |
Sider (fra-til) | 1753-1765 |
Antal sider | 13 |
ISSN | 2150-8097 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australien Varighed: 5 sep. 2022 → 9 sep. 2022 |
Konference
Konference | 48th International Conference on Very Large Data Bases, VLDB 2022 |
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Land/Område | Australien |
By | Sydney |
Periode | 05/09/2022 → 09/09/2022 |
Bibliografisk note
Funding Information:This work was supported by EU MSCA No. 882232, NSFC No. 61802163, and DIREC, funded by Innovation Fund Denmark. The authors thank Xinle Jiang for preprocessing GeoLife data. Bo Tang is the corresponding author, and he is affiliated with the Research Institute of Trustworthy Autonomous Systems, SUSTech.
Publisher Copyright:
© 2022, VLDB Endowment.
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The Project of Efficient and Error-bounded Spatiotemporal Quantile Monitoring in Edge Computing Environments
Li, H. (Ophavsperson), Yi, L. (Ophavsperson), Tang, B. (Ophavsperson), Lu, H. (Ophavsperson) & Jensen, C. S. (Ophavsperson), Zenodo, 25 maj 2022
DOI: 10.5281/zenodo.7053904, https://zenodo.org/record/7053904
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