Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City
With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily re...
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sg-smu-ink.sis_research-30822014-02-26T10:13:41Z Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City KIM, Byoungjip LEE, SangJeong LEE, Youngki HWANG, Inseok RHEE, Yunseok SONG, Junehwa With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison. 2011-11-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/2083 info:doi/10.1016/j.jss.2011.06.068 http://dx.doi.org/10.1016/j.jss.2011.06.068 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Location-based services Mobility pattern monitoring Location stream processing Complex event processing Software Engineering |
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Location-based services Mobility pattern monitoring Location stream processing Complex event processing Software Engineering KIM, Byoungjip LEE, SangJeong LEE, Youngki HWANG, Inseok RHEE, Yunseok SONG, Junehwa Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
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With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison. |
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text |
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KIM, Byoungjip LEE, SangJeong LEE, Youngki HWANG, Inseok RHEE, Yunseok SONG, Junehwa |
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KIM, Byoungjip LEE, SangJeong LEE, Youngki HWANG, Inseok RHEE, Yunseok SONG, Junehwa |
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KIM, Byoungjip |
title |
Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
title_short |
Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
title_full |
Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
title_fullStr |
Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
title_full_unstemmed |
Mobiiscape: Middleware Support for Scalable Mobility Pattern Monitoring of Moving Objects in a Large-Scale City |
title_sort |
mobiiscape: middleware support for scalable mobility pattern monitoring of moving objects in a large-scale city |
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Institutional Knowledge at Singapore Management University |
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2011 |
url |
https://ink.library.smu.edu.sg/sis_research/2083 http://dx.doi.org/10.1016/j.jss.2011.06.068 |
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1770571795138609152 |