Spatial keyword queries (SKQ), which consider both the distance and the keyword similarity of objects, have received a growing number of attention in real life. However, most of the existing SKQ methods are either focused on finding individual objects that each satisfy a query requirement or limited in Euclidean space. The paper takes the first step to address the issue of processing Collective Spatial Keyword Queries in Road Networks (CoSKQRN). Two efficient algorithms called AppM and OptM are proposed. In particular, AppM method is used to get the approximate result set with a relatively low cost, and OptM is to get the optimal query result set with a reasonable cost. Finally, simulation experiments on a real road network and a geo-textual dataset are conducted to demonstrate the performance of our proposed algorithms.
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