A new algorithm for m-closest keywords query over spatial Web with grid partitioning - Yuan Qiu; Ohmori, Tadashi; Shintani, Takahiko; Fujita, Hideyuki (2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015)
In this paper, we focus on the issue of the m-closest keywords (mCK) query over spatial data in the Web. The mCK query is a problem to find the optimal set of records in the sense that they are the spatially-closest records that satisfy m user-given keywords. The mCK query was proposed by Zhang et al[1]. They assumed a specialized R*-tree to store all records and proposed an Apriori-based enumeration of MBR-combinations. However, this assumption of the prepared R*-tree is not always applicable; Twitter or Flickr provides only records having position information without any prepared data-partitioning. Many services like Google Maps only provide grid partitioning at most. Thus, in this paper, we do not expect any prepared data-partitioning, but assume that we create a grid partitioning from necessary data only when an mCK query is given. Under this assumption, we propose a new search-strategy termed Diameter Candidate Check (DCC), and show that DCC can efficiently find a better set of grid-cells at an earlier stage of search, thereby reducing search space greatly.