Abstract
Identifying prospective customers is an important aspect of marketing research. In this paper, we provide support for a new type of query, the Reverse Top-k Geo-Social Keyword (RkGSK) query. This query takes into account spatial, textual, and social information, and finds prospective customers for geotagged objects. As an example, a restaurant manager might apply the query to find prospective customers. To address this, we propose a hybrid index, the GIM-Tree, which indexes locations, keywords, and social information of geo-Tagged users and objects, and then, using the GIM-Tree, we present efficient RkGSK query processing algorithms that exploit several pruning strategies. The effectiveness of RkGSK retrieval is characterized via a case study, and extensive experiments using real datasets offer insight into the efficiency of the proposed index and algorithms.
Original language | English |
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Title of host publication | Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 |
Number of pages | 12 |
Publisher | IEEE |
Publication date | 16 May 2017 |
Pages | 387-398 |
Article number | 7929993 |
ISBN (Electronic) | 9781509065431 |
DOIs | |
Publication status | Published - 16 May 2017 |
Event | 33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States Duration: 19 Apr 2017 → 22 Apr 2017 |
Conference
Conference | 33rd IEEE International Conference on Data Engineering, ICDE 2017 |
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Country/Territory | United States |
City | San Diego |
Period | 19/04/2017 → 22/04/2017 |