IR-Tree: An Efficient Index for Geographic Document Search
Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and s...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2011
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/1354 https://ink.library.smu.edu.sg/context/sis_research/article/2353/viewcontent/paper_zhisheng.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-2353 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-23532015-12-25T07:39:54Z IR-Tree: An Efficient Index for Geographic Document Search LI, Zhisheng LEE, Ken C. K. ZHENG, Baihua LEE, Wang-Chien LEE, Dik Lun WANG, Xufa Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches. 2011-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1354 info:doi/10.1109/TKDE.2010.149 https://ink.library.smu.edu.sg/context/sis_research/article/2353/viewcontent/paper_zhisheng.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Geographic document search index search algorithm and IR-tree. Computer Sciences Databases and Information Systems Geographic Information Sciences |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
Geographic document search index search algorithm and IR-tree. Computer Sciences Databases and Information Systems Geographic Information Sciences |
spellingShingle |
Geographic document search index search algorithm and IR-tree. Computer Sciences Databases and Information Systems Geographic Information Sciences LI, Zhisheng LEE, Ken C. K. ZHENG, Baihua LEE, Wang-Chien LEE, Dik Lun WANG, Xufa IR-Tree: An Efficient Index for Geographic Document Search |
description |
Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches. |
format |
text |
author |
LI, Zhisheng LEE, Ken C. K. ZHENG, Baihua LEE, Wang-Chien LEE, Dik Lun WANG, Xufa |
author_facet |
LI, Zhisheng LEE, Ken C. K. ZHENG, Baihua LEE, Wang-Chien LEE, Dik Lun WANG, Xufa |
author_sort |
LI, Zhisheng |
title |
IR-Tree: An Efficient Index for Geographic Document Search |
title_short |
IR-Tree: An Efficient Index for Geographic Document Search |
title_full |
IR-Tree: An Efficient Index for Geographic Document Search |
title_fullStr |
IR-Tree: An Efficient Index for Geographic Document Search |
title_full_unstemmed |
IR-Tree: An Efficient Index for Geographic Document Search |
title_sort |
ir-tree: an efficient index for geographic document search |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2011 |
url |
https://ink.library.smu.edu.sg/sis_research/1354 https://ink.library.smu.edu.sg/context/sis_research/article/2353/viewcontent/paper_zhisheng.pdf |
_version_ |
1770570975053611008 |