Top-k point of interest retrieval using standard indexes

Abstract

With the proliferation of Internet-connected, location-aware mobile devices, such as smartphones, we are also witnessing a proliferation and increased use of map-based services that serve information about relevant Points of Interest (PoIs) to their users. We provide an efficient and practical foundation for the processing of queries that take a keyword and a spatial region as arguments and return the k most relevant PoIs that belong to the region, which may be the part of the map covered by the user's screen. The paper proposes a novel technique that encodes the spatio-textual part of a PoI as a compact bit string. This technique extends an existing spatial encoding to also encode the textual aspect of a PoI in compressed form. The resulting bit strings may then be indexed using index structures such as B-trees or hashing that are standard in DBMSs and key-value stores. As a result, it is straightforward to support the proposed functionality using existing data management systems. The paper also proposes a novel top-k query algorithm that merges partial results while providing an exact result. An empirical study with real-world data indicates that the proposed techniques enable excellent indexing and query execution performance on a standard DBMS.