Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub

Tsuruoka, Shohei; Amagata, Daichi; Nishio, Shunya; Hara, Takahiro

doi:https://doi.org/10.1145/3397536.3422199

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub

Tsuruoka, Shohei; Amagata, Daichi; Nishio, Shunya et al.

GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2020, 111-114

Number of Access:786（2025-11-27 15:12 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/92859

Link to primary information

File	Format	Terms of use	Size	Views	Date.Available	Description	information
GISProcACMIntSympAdvGeogrInfSyst_111	pdf	None	983 KB	130	2023.10.31

Item Information

Output File Export EndNote Basic Export Mendeley

Title	Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
Creator	CreatorName Tsuruoka, Shohei
	NRID 1000040770649 1000040770649 researchmap permalink amgt amgt Scopus Author ID 55811167300 55811167300 CreatorName Amagata, Daichi
	CreatorName Nishio, Shunya
	NRID 1000020294043 1000020294043 ORCID 0000-0003-4807-3156 0000-0003-4807-3156 researchmap permalink hara-osakau hara-osakau Scopus Author ID 7404213547 7404213547 CreatorName Hara, Takahiro
Subject	kNN queries
Subject	location-aware Pub/Sub
Abstract	Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given m workers, we divide a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. We allow an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution.
Conference Information	Conference Name 28th International Conference on Advances in Geographic Information Systems Conference Sequence 28 Conference Date November 3rd to November 6th, 2020 Conference Place Seattle Conference Country United States of America
Publisher	Association for Computing Machinery
Source Title	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Page	111-114
Date of Issued	2020-11-03
Language	English
Handle URL	https://hdl.handle.net/11094/92859
Relation.isVersionOf	DOI (Publisher Version) https://doi.org/10.1145/3397536.3422199
Relation.isPartOf	ISBN 9781450380195
Access Rights	open access
CopyRight	© 2020 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Tsuruoka S., Amagata D., Nishio S., et al. Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 111 (2020); https://doi.org/10.1145/3397536.3422199.
oaire:version	Accepted Manuscript
Category	会議発表論文 Conference Paper

Resource Type	conference paper
Local Type	Conference Paper
DCMI Type	Text
DCTERMS.bibliographicCitation	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. 2020, p. 111-114
DC.title	Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
DC.creator	Tsuruoka, Shohei
	Amagata, Daichi
	Nishio, Shunya
	Hara, Takahiro
DC.publisher	Association for Computing Machinery
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2020-11-03
DC.identifier	https://doi.org/10.1145/3397536.3422199
DC.subject	kNN queries
DC.subject	location-aware Pub/Sub
DCTERMS.abstract	Recent applications employ publish/subscribe (Pub/Sub) systems so that publishers can easily receive attentions of customers and subscribers can monitor useful information generated by publishers. Due to the prevalence of smart devices and social networking services, a large number of objects that contain both spatial and keyword information have been generated continuously, and the number of subscribers also continues to increase. This poses a challenge to Pub/Sub systems: they need to continuously extract useful information from massive objects for each subscriber in real time. In this paper, we address the problem of k nearest neighbor monitoring on a spatial-keyword data stream for a large number of subscriptions. To scale well to massive objects and subscriptions, we propose a distributed solution. Given m workers, we divide a set of subscriptions into m disjoint subsets based on a cost model so that each worker has almost the same kNN-update cost, to maintain load balancing. We allow an arbitrary approach to updating kNN of each subscription, so with a suitable in-memory index, our solution can accelerate update efficiency by pruning irrelevant subscriptions for a given new object. We conduct experiments on real datasets, and the results demonstrate the efficiency and scalability of our solution.
DC.rights	© 2020 ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Tsuruoka S., Amagata D., Nishio S., et al. Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 111 (2020); https://doi.org/10.1145/3397536.3422199.
DC.format	92859
citation_title	Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub
citation_author	Tsuruoka, Shohei
	Amagata, Daichi
	Nishio, Shunya
	Hara, Takahiro
citation_publisher	Association for Computing Machinery
citation_language	English
citation_date	2020-11-03
citation_journal_title	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
citation_firstpage	111
citation_lastpage	114
citation_doi	https://doi.org/10.1145/3397536.3422199
citation_public_url	https://hdl.handle.net/11094/92859

Distributed Spatial-Keyword kNN Monitoring for Location-aware Pub/Sub

Link to primary information

Item Information

Show details