A new algorithm for m-closest keywords query over spatial Web with grid partitioning

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 a...

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Veröffentlicht in:2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) S. 1 - 8
Hauptverfasser: Yuan Qiu, Ohmori, Tadashi, Shintani, Takahiko, Fujita, Hideyuki
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2015
Schlagworte:
Google
Itemsets
Nickel
Partitioning algorithms
Search problems
Spatial databases
Twitter
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Zusammenfassung: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.
DOI:10.1109/SNPD.2015.7176250