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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
assessed the efficacy of mobile CNMSs and to discuss the findings with respect to future work. A systematic literature review and meta-analysis was conducted to address these objectives. Consistent with prior work, two efficacy metrics were applied: response rate and response delay. A keyword-based search strategy was used
network traffic costs when using the traditional search schemes. This paper addresses these issues by proposing an efficient encrypted data search (EnDAS) scheme as a mobile cloud service. This innovative scheme uses a lightweight trapdoor (encrypted keyword) compression method, which optimizes the data communication process
shops), how can we find sets of objects that are both spatially and textually relevant to a query? Most of existing studies solve the problem by requiring that all query keywords are covered by the returned objects and then rank the sets by spatial proximity. The needs for identifying sets with more textually relevant
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.