Location-aware top-κ term publish/subscribe

Lisi Chen; Shuo Shang; Zhiwei ZHANG; Xin Cao; Christian S. Jensen; Panos Kalnis

doi:10.1109/ICDE.2018.00073

Location-aware top-κ term publish/subscribe

Lisi Chen, Shuo Shang^*, Zhiwei ZHANG, Xin Cao, Christian S. Jensen, Panos Kalnis

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

41 Citations (Scopus)

Abstract

Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.

Original language	English
Title of host publication	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
Publisher	IEEE
Pages	749-760
Number of pages	12
ISBN (Electronic)	9781538655207
DOIs	https://doi.org/10.1109/ICDE.2018.00073
Publication status	Published - 24 Oct 2018
Event	34th IEEE International Conference on Data Engineering, ICDE 2018 - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding

Publication series

Name	Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018

Conference

Conference	34th IEEE International Conference on Data Engineering, ICDE 2018
Country/Territory	France
City	Paris
Period	16/04/18 → 19/04/18
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding

Scopus Subject Areas

Hardware and Architecture
Information Systems
Information Systems and Management

User-Defined Keywords

publish
Spatial
stream
Subscribe
Temporal

Access to Document

10.1109/ICDE.2018.00073

Cite this

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title = "Location-aware top-κ term publish/subscribe",

abstract = "Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.",

keywords = "publish, Spatial, stream, Subscribe, Temporal",

author = "Lisi Chen and Shuo Shang and Zhiwei ZHANG and Xin Cao and Jensen, {Christian S.} and Panos Kalnis",

note = "Funding Information: This work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.; 34th IEEE International Conference on Data Engineering, ICDE 2018 ; Conference date: 16-04-2018 Through 19-04-2018",

year = "2018",

month = oct,

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doi = "10.1109/ICDE.2018.00073",

language = "English",

series = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

publisher = "IEEE",

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booktitle = "Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/8476188/proceeding",

}

Chen, L, Shang, S, ZHANG, Z, Cao, X, Jensen, CS & Kalnis, P 2018, Location-aware top-κ term publish/subscribe. in Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018., 8509294, Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018, IEEE, pp. 749-760, 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, 16/04/18. https://doi.org/10.1109/ICDE.2018.00073

Location-aware top-κ term publish/subscribe. / Chen, Lisi; Shang, Shuo; ZHANG, Zhiwei et al.

Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018. IEEE, 2018. p. 749-760 8509294 (Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

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AU - Shang, Shuo

AU - ZHANG, Zhiwei

AU - Cao, Xin

AU - Jensen, Christian S.

AU - Kalnis, Panos

N1 - Funding Information: This work was supported by the grant of the Hong Kong Research Grants Council, Hong Kong SAR, China, No. 12258116 and the Nation Nature Science Foundation of China, China, No. 61602395.

PY - 2018/10/24

Y1 - 2018/10/24

N2 - Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.

AB - Massive amount of data that contain spatial, textual, and temporal information are being generated at a high scale. These spatio-Temporal documents cover a wide range of topics in local area. Users are interested in receiving local popular terms from spatio-Temporal documents published with a specified region. We consider the Top-k Spatial-Temporal Term (ST2) Subscription. Given an ST2 subscription, we continuously maintain up-To-date top-k most popular terms over a stream of spatio-Temporal documents. The ST2 subscription takes into account both frequency and recency of a term generated from spatio-Temporal document streams in evaluating its popularity. We propose an efficient solution to process a large number of ST2 subscriptions over a stream of spatio-Temporal documents. The performance of processing ST2 subscriptions is studied in extensive experiments based on two real spatio-Temporal datasets.

KW - publish

KW - Spatial

KW - stream

KW - Subscribe

KW - Temporal

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ER -

Location-aware top-κ term publish/subscribe

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

Access to Document

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