Multi-IRS: Multiple trees indexing for generic location-aware rank query
Conference proceedings article
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Publication Details
Author list: Buranasaksee U., Porkaew K.
Publisher: Hindawi
Publication year: 2016
Start page: 518
End page: 524
Number of pages: 7
ISBN: 9789811100086
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
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.
Keywords
Generic, Index, Location-aware, Multiple, Query
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