Publication Date

Spring 2015

Document Type

Project Summary

Degree Name

Master of Science

Department

Computer Science

First Advisor

Kong-Cheng Wong, Ph.D.

Second Advisor

Soon-Ok Park, Ph.D.

Third Advisor

(Clare) Xueqing Tang, Ph.D.

Abstract

Conventional spatial queries, such as range search and nearest neighbor retrieval, involve only conditions on objects’ geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbor query would instead ask for the restaurant that is the closest among those whose menus contain “steak, spaghetti, brandy” all at the same time. Currently the best solution to such queries is based on the IR2-tree, which, as shown in this paper, has a few deficiencies that seriously impact its efficiency. Motivated by this, we develop a new access method called the spatial inverted index that extends the conventional inverted index to cope with multidimensional data, and comes with algorithms that can answer nearest neighbor queries with keywords in real time. As verified by experiments, the proposed techniques outperform the IR2-tree in query response time significantly, often by a factor of orders of magnitude.

Comments

Co-authored capstone with authors listed in alphabetical order by OPUS staff.

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