A Unified Processing Paradigm for Interactive Location-based Web Search


This paper studies the location-based web search and aims to build a unified processing paradigm for two purposes - (1) efficiently support each of the various types of location-based queries (kNN query,top-k spatial-textual query, etc.) on two major forms of geo-tagged data, i.e., spatial point data such as geo-tagged web documents, and spatial trajectory data such as a sequence of geo-tagged travel blogs by a user; (2) support interactive search to provide quick response for a query session, within which a user usually keeps refining her query by either issuing different query types or specifying different constraints (e.g., adding a keyword and/or location, changing the choice of k, etc.) until she finds the desired results. To achieve this goal, we first propose a general Top-k query called Monotone Aggregate Spatial Keyword query-MASK, which is able to cover most types of location-based web search. Next, we develop a unified indexing (called Textual-Grid-Point Inverted Index) and query processing paradigm (called ETAIL Algorithm) to answer a single MASK query efficiently. Furthermore, we extend ETAIL to provide interactive search for multiple queries within one query session, by exploiting the commonality of textual and/or spatial dimension among queries. Last, extensive experiments on four real datasets verify the robustness and efficiency of our approach.

In the 11th ACM International Conference on Web Search and Data Mining (WSDM)