Multi-IRS: Multiple trees indexing for generic location-aware rank query

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Buranasaksee U., Porkaew K.

ผู้เผยแพร่: Hindawi

ปีที่เผยแพร่ (ค.ศ.): 2016

หน้าแรก: 518

หน้าสุดท้าย: 524

จำนวนหน้า: 7

ISBN: 9789811100086

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982798394&partnerID=40&md5=db1d89cde921128ceeb88e81201290c6

ภาษา: English-Great Britain (EN-GB)

บทคัดย่อ

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.

คำสำคัญ

Generic, Index, Location-aware, Multiple, Query