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:741（2025-10-07 07:33 Counts）

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

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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
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