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Multi-IRS: Multiple Trees Indexing for Generic Location-Aware Rank Query

Abstract— The mobile usage nowaday makes the data on the Internet becomes more location-aware. Searching two-dimensional space with the text requires a powerful index structure that can combine two data types in the same index. Though there have been many indexes proposed to solve location-aware rank query problem by combining such information within the same data structure, in the big data era, many new datatypes are introduced and required to search with the geolocation information. Integrating multiple datatypes to spatial-textual objects requires a new index structure that can efficiently perform searching those generic datatypes. Though there were some existing studies that proposed the framework such as inverted Rtree with synopses (IRS), the framework is not able to achieve optimized performance due to the index creation process remains same as the traditional method. This paper presents the multiple trees indexing that can improve the optimization of the index structure based on the given query at the runtime. In the experimental, our proposed method can significantly outperform the state-of-the-art method on the real dataset.

Index Terms— multiple, index, generic, location-aware, query.

Utharn Buranasaksee
Rajamangala University of Technology Suvarnabhumi, THAILAND
Kriengkrai Porkaew
King Mongkut’s University of Technology Thonburi, THAILAND

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Cite: Utharn Buranasaksee, Kriengkrai Porkaew, "Multi-IRS: Multiple Trees Indexing for Generic Location-Aware Rank Query," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 518-524, Tokyo, 17-19 June, 2016.