Q-ASSF

Query-adaptive semantic stream filtering

Jinho Shin, Sungkwang Eom, Kyong Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In this paper, we address the problem of processing semantic data streams. The semantic annotation of sensor data is one of the solutions to the heterogeneous nature of sensor data streams. Existing systems for publishing semantic streaming data collect stream data and transmit the semantic streaming data to query engines regardless of the queries registered in the query engines. As a large number of sensing devices become available, there is an increasing amount of the stream data, resulting in the performance degradation of a query engine. To remedy this problem, we propose a query-adaptive method of filtering semantic streams. The proposed method filters out sensors and semantic streaming data, which are not related with queries registered in a semantic stream query engine. The approach fairly reduces the data size necessary to answer semantic stream queries and consequently improves the performance of the query processing. To demonstrate the efficiency of our proposal, we present extensive experimental performance evaluations under a variety of sensor streams and query types. Experimental results show that the proposed method dramatically improves the performance of query processing compared to a non-filtering approach.

Original languageEnglish
Title of host publicationProceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages101-108
Number of pages8
ISBN (Electronic)9781479979356
DOIs
Publication statusPublished - 2015 Jan 1
Event9th IEEE International Conference on Semantic Computing, IEEE ICSC 2015 - Anaheim, United States
Duration: 2015 Feb 72015 Feb 9

Other

Other9th IEEE International Conference on Semantic Computing, IEEE ICSC 2015
CountryUnited States
CityAnaheim
Period15/2/715/2/9

Fingerprint

Semantics
Engines
Query processing
Sensors
Degradation
Processing

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Shin, J., Eom, S., & Lee, K. H. (2015). Q-ASSF: Query-adaptive semantic stream filtering. In Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015 (pp. 101-108). [7050786] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICOSC.2015.7050786
Shin, Jinho ; Eom, Sungkwang ; Lee, Kyong Ho. / Q-ASSF : Query-adaptive semantic stream filtering. Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 101-108
@inproceedings{4ad49c7f7e6742f485f8e10e0add8e93,
title = "Q-ASSF: Query-adaptive semantic stream filtering",
abstract = "In this paper, we address the problem of processing semantic data streams. The semantic annotation of sensor data is one of the solutions to the heterogeneous nature of sensor data streams. Existing systems for publishing semantic streaming data collect stream data and transmit the semantic streaming data to query engines regardless of the queries registered in the query engines. As a large number of sensing devices become available, there is an increasing amount of the stream data, resulting in the performance degradation of a query engine. To remedy this problem, we propose a query-adaptive method of filtering semantic streams. The proposed method filters out sensors and semantic streaming data, which are not related with queries registered in a semantic stream query engine. The approach fairly reduces the data size necessary to answer semantic stream queries and consequently improves the performance of the query processing. To demonstrate the efficiency of our proposal, we present extensive experimental performance evaluations under a variety of sensor streams and query types. Experimental results show that the proposed method dramatically improves the performance of query processing compared to a non-filtering approach.",
author = "Jinho Shin and Sungkwang Eom and Lee, {Kyong Ho}",
year = "2015",
month = "1",
day = "1",
doi = "10.1109/ICOSC.2015.7050786",
language = "English",
pages = "101--108",
booktitle = "Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Shin, J, Eom, S & Lee, KH 2015, Q-ASSF: Query-adaptive semantic stream filtering. in Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015., 7050786, Institute of Electrical and Electronics Engineers Inc., pp. 101-108, 9th IEEE International Conference on Semantic Computing, IEEE ICSC 2015, Anaheim, United States, 15/2/7. https://doi.org/10.1109/ICOSC.2015.7050786

Q-ASSF : Query-adaptive semantic stream filtering. / Shin, Jinho; Eom, Sungkwang; Lee, Kyong Ho.

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 101-108 7050786.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Q-ASSF

T2 - Query-adaptive semantic stream filtering

AU - Shin, Jinho

AU - Eom, Sungkwang

AU - Lee, Kyong Ho

PY - 2015/1/1

Y1 - 2015/1/1

N2 - In this paper, we address the problem of processing semantic data streams. The semantic annotation of sensor data is one of the solutions to the heterogeneous nature of sensor data streams. Existing systems for publishing semantic streaming data collect stream data and transmit the semantic streaming data to query engines regardless of the queries registered in the query engines. As a large number of sensing devices become available, there is an increasing amount of the stream data, resulting in the performance degradation of a query engine. To remedy this problem, we propose a query-adaptive method of filtering semantic streams. The proposed method filters out sensors and semantic streaming data, which are not related with queries registered in a semantic stream query engine. The approach fairly reduces the data size necessary to answer semantic stream queries and consequently improves the performance of the query processing. To demonstrate the efficiency of our proposal, we present extensive experimental performance evaluations under a variety of sensor streams and query types. Experimental results show that the proposed method dramatically improves the performance of query processing compared to a non-filtering approach.

AB - In this paper, we address the problem of processing semantic data streams. The semantic annotation of sensor data is one of the solutions to the heterogeneous nature of sensor data streams. Existing systems for publishing semantic streaming data collect stream data and transmit the semantic streaming data to query engines regardless of the queries registered in the query engines. As a large number of sensing devices become available, there is an increasing amount of the stream data, resulting in the performance degradation of a query engine. To remedy this problem, we propose a query-adaptive method of filtering semantic streams. The proposed method filters out sensors and semantic streaming data, which are not related with queries registered in a semantic stream query engine. The approach fairly reduces the data size necessary to answer semantic stream queries and consequently improves the performance of the query processing. To demonstrate the efficiency of our proposal, we present extensive experimental performance evaluations under a variety of sensor streams and query types. Experimental results show that the proposed method dramatically improves the performance of query processing compared to a non-filtering approach.

UR - http://www.scopus.com/inward/record.url?scp=84925585022&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84925585022&partnerID=8YFLogxK

U2 - 10.1109/ICOSC.2015.7050786

DO - 10.1109/ICOSC.2015.7050786

M3 - Conference contribution

SP - 101

EP - 108

BT - Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Shin J, Eom S, Lee KH. Q-ASSF: Query-adaptive semantic stream filtering. In Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing, IEEE ICSC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 101-108. 7050786 https://doi.org/10.1109/ICOSC.2015.7050786