Keyword Search in Spatial Databases: Towards Searching by Document

TLDR

Spatial databases associate each tuple with descriptive keywords. The study introduces the m‑closest keywords (mCK) query to retrieve the spatially nearest tuples that match a user‑specified set of keywords. To answer mCK queries efficiently, the authors extend the R*-tree into a bR*-tree index and employ a priori search strategies with distance and keyword mutex constraints to prune the search space. Experiments show that the mCK query effectively geotags documents and that the proposed strategy significantly reduces response time while scaling well with the number of keywords.

Abstract

This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.