Unsupervised User Similarity Mining in GSM Sensor Networks
Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community fin...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/589610 |
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| _version_ | 1832562558989500416 |
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| author | Shafqat Ali Shad Enhong Chen |
| author_facet | Shafqat Ali Shad Enhong Chen |
| author_sort | Shafqat Ali Shad |
| collection | DOAJ |
| description | Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user’s actual movement prediction, and context awareness. However, significant places extraction and user’s actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining. |
| format | Article |
| id | doaj-art-bbcef07c46354b18bacb9b55b0d8544d |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-bbcef07c46354b18bacb9b55b0d8544d2025-02-03T01:22:23ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/589610589610Unsupervised User Similarity Mining in GSM Sensor NetworksShafqat Ali Shad0Enhong Chen1Department of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, Anhui 230027, ChinaDepartment of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, Anhui 230027, ChinaMobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user’s actual movement prediction, and context awareness. However, significant places extraction and user’s actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.http://dx.doi.org/10.1155/2013/589610 |
| spellingShingle | Shafqat Ali Shad Enhong Chen Unsupervised User Similarity Mining in GSM Sensor Networks The Scientific World Journal |
| title | Unsupervised User Similarity Mining in GSM Sensor Networks |
| title_full | Unsupervised User Similarity Mining in GSM Sensor Networks |
| title_fullStr | Unsupervised User Similarity Mining in GSM Sensor Networks |
| title_full_unstemmed | Unsupervised User Similarity Mining in GSM Sensor Networks |
| title_short | Unsupervised User Similarity Mining in GSM Sensor Networks |
| title_sort | unsupervised user similarity mining in gsm sensor networks |
| url | http://dx.doi.org/10.1155/2013/589610 |
| work_keys_str_mv | AT shafqatalishad unsupervisedusersimilarityminingingsmsensornetworks AT enhongchen unsupervisedusersimilarityminingingsmsensornetworks |