On Generalizing Collective Spatial Keyword Queries (Extended Abstract)
Harry Kai-Ho Chan, Cheng Zhi Long, Raymond Chi-Wing Wong · 2019
With the proliferation of spatial-textual data such as location-based services and geo-tagged websites, spatial keyword queries are ubiquitous in real life. One example of spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find, for a given query consisting a query location and several query keywords, a set of objects which covers the query keywords collectively and has the smallest cost wrt the query location. Quite a few cost functions have been proposed for CoSKQ and correspondingly, different approaches have been developed. However, given these cost functions in different forms and approaches in different structures, one could hardly compare existing cost functions systematically and needs to implement all approaches in order to tackle the CoSKQ problem with different cost functions, which is effort-consuming. In this paper, we design a unified cost function which generalizes the majority of existing cost functions for CoSKQ and develop a unified approach which works as well as (and sometimes better than) best-known approaches based on different cost functions. Experiments were conducted on both real and synthetic datasets which verified our proposed approach.