Continuous spatial keyword query processing over geo-textual data streams

Real-time processing of spatial keyword queries has been playing an indispensable role in location-based services. In this light, we propose and study a novel problem of processing continuous spatial keyword queries over geo-textual data streams. We define a new location-based continuously query that enable users to define personalized spatial requirement and textual requirement. Each query continuously feeds users with geo-textual objects that satisfy both spatial and textual requirements set by the query. To process massive-scale continuous spatial keyword queries efficiently, we develop a Continuous Spatial Keyword Query Matching (CSKQM) framework that takes a stream of queries as input and applies hierarchical dynamic grid cells to index each batch of queries. We also propose effective index update algorithm and efficient geo-textual object matching algorithm to process massive-scale continuous spatial keyword queries simultaneously over a stream of geo-textual objects. We conduct comprehensive experimental study on two real datasets to verify the performance of the CSKQM framework.