Mining multivariate spatiotemporal patterns from heterogeneous mobility data
Mobility data mining in the form of trajectory data mining has been extensively investigated in recent years. Predictive modeling and pattern discovery approaches have been proposed to predict movements and locations, and to extract useful trajectory and location patterns. Nowadays, mobility data co...
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| Format: | Conference Paper |
| Language: | English |
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2013
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| Online Access: | https://hdl.handle.net/10356/100253 http://hdl.handle.net/10220/16277 |
| _version_ | 1826124445716054016 |
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| author | Ho, Shen-Shyang. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Ho, Shen-Shyang. |
| author_sort | Ho, Shen-Shyang. |
| collection | NTU |
| description | Mobility data mining in the form of trajectory data mining has been extensively investigated in recent years. Predictive modeling and pattern discovery approaches have been proposed to predict movements and locations, and to extract useful trajectory and location patterns. Nowadays, mobility data consist of not only trajectory data. Mobility data from smart phones include measurements such as call duration/time, call type, digital media consumption, calendar information, apps usage, social interactions, and mobile browsing. These heterogeneous multivariate data allow one to discover interesting and more complex behavioral patterns and rules in terms of space and time.
In this paper, we investigate spatiotemporal rule mining on heterogeneous multivariate mobility data. We propose a systematic approach consisting of three main steps: data fusion, frequent temporal multivariate-location extraction, and rule generation. In particular, we explore the task of extracting multivariate spatiotemporal patterns corresponding to the "where", "when", and "who" queries (and their combinations) related to phone call variables collected from smart phone users. Experimental results on the data from Nokia Mobile Data Challenge is used to show the feasibility and usefulness of our proposed approach. |
| first_indexed | 2024-10-01T06:20:45Z |
| format | Conference Paper |
| id | ntu-10356/100253 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:20:45Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1002532020-05-28T07:18:12Z Mining multivariate spatiotemporal patterns from heterogeneous mobility data Ho, Shen-Shyang. School of Computer Engineering International Conference on Advances in Geographic Information Systems (20th : 2012) DRNTU::Engineering::Computer science and engineering Mobility data mining in the form of trajectory data mining has been extensively investigated in recent years. Predictive modeling and pattern discovery approaches have been proposed to predict movements and locations, and to extract useful trajectory and location patterns. Nowadays, mobility data consist of not only trajectory data. Mobility data from smart phones include measurements such as call duration/time, call type, digital media consumption, calendar information, apps usage, social interactions, and mobile browsing. These heterogeneous multivariate data allow one to discover interesting and more complex behavioral patterns and rules in terms of space and time. In this paper, we investigate spatiotemporal rule mining on heterogeneous multivariate mobility data. We propose a systematic approach consisting of three main steps: data fusion, frequent temporal multivariate-location extraction, and rule generation. In particular, we explore the task of extracting multivariate spatiotemporal patterns corresponding to the "where", "when", and "who" queries (and their combinations) related to phone call variables collected from smart phone users. Experimental results on the data from Nokia Mobile Data Challenge is used to show the feasibility and usefulness of our proposed approach. 2013-10-04T06:53:17Z 2019-12-06T20:19:10Z 2013-10-04T06:53:17Z 2019-12-06T20:19:10Z 2012 2012 Conference Paper Ho, S. S. (2012). Mining multivariate spatiotemporal patterns from heterogeneous mobility data. Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp.486-489. https://hdl.handle.net/10356/100253 http://hdl.handle.net/10220/16277 10.1145/2424321.2424396 en |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Ho, Shen-Shyang. Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title | Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title_full | Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title_fullStr | Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title_full_unstemmed | Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title_short | Mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| title_sort | mining multivariate spatiotemporal patterns from heterogeneous mobility data |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/100253 http://hdl.handle.net/10220/16277 |
| work_keys_str_mv | AT hoshenshyang miningmultivariatespatiotemporalpatternsfromheterogeneousmobilitydata |