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Density-based spatiotemporal Clustering Algorithm for Extracting Bursty Areas from Georeferenced Documents

Density-based Spatiotemporal Clustering Algorithm for Extrac...

引用

IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Tamura, Keiichi Ichimura, Takumi Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan Prefecture Univ Hiroshima Fac Management & Informat Syst Hiroshima Japan

ISBN: (纸本)9781479906529

Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, extracting bursty areas associated with local topics and events from georeferenced documents is one of the most important challenges in different application domains. In this paper, a novel spatiotemporal clustering algorithm, called the (epsilon, tau)-density-based spatiotemporal clustering algorithm, for extracting bursty areas from georeferenced documents is proposed. The proposed clustering algorithm can recognize not only temporally-separated but also spatially-separated clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site are used. The experimental results show that the (epsilon, tau)-density-based spatiotemporal clustering algorithm can extract bursty areas as (epsilon, tau)-density-based spatiotemporal clusters associated with local topics and events.

关键词： spatiotemporal clustering algorithm Density-based clustering Georeferenced document spatiotemporal data stream Topic and event detection

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Design and Implementation of a System for Environmental Monitoring Sensor Network

引用

9th Asia-Pacific Web Conference/8th International Conference on Web-Age Information Management (APWeb/WAIM 2007)

作者： Lee, Yang Koo Jung, Young Jin Ryu, Keun Ho Chungbuk Natl Univ Database Bioinformat Lab Cheongju South Korea

ISBN: (纸本)9783540729082

In this paper, we propose a system architecture for handling and storing sensor data stream in real-time to support the spatial and/or temporal queries besides continuous queries. We exploit a segment-based method to store the sensor data stream and reduce the managed tuples without any loss Of information, which lead to the improvement of the accuracy of query results. In addition, we offer a method to reduce the cost of join operations in processing spatiotemporal queries by filtering out the list of irrelevant sensors from the query range before making the join operation. We then present a design of the system architecture for processing spatial and/or temporal queries. Finally, we implement a climate monitoring application system.

关键词： Wireless Sensor Network Sensor data stream spatiotemporal data stream Continuous Query Historical Query

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Density-based spatiotemporal Clustering Algorithm for Extracting Bursty Areas from Georeferenced Documents

Density-based Spatiotemporal Clustering Algorithm for Extrac...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Keiichi Tamura Takumi Ichimura Graduate School of Information Sciences Hiroshima City University Hiroshima Japan Faculty of Management and Information Systems Prefectural University of Hiroshima Hiroshima Japan

ISBN: (纸本)9781479906505

Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, extracting bursty areas associated with local topics and events from georeferenced documents is one of the most important challenges in different application domains. In this paper, a novel spatiotemporal clustering algorithm, called the (∈,T)-density-based spatiotemporal clustering algorithm, for extracting bursty areas from georeferenced documents is proposed. The proposed clustering algorithm can recognize not only temporally-separated but also spatially-separated clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site are used. The experimental results show that the (∈, T)-density-based spatiotemporal clustering algorithm can extract bursty areas as (∈, T)-density-based spatiotemporal clusters associated with local topics and events.

关键词： spatiotemporal clustering algorithm Density-based clustering Georeferenced document spatiotemporal data stream Topic and event detection Clustering algorithms Social Media POSITION INFORMATION

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