Paper Info
Title
Keyword Search in Spatial Databases: Towards Searching by Document
Abstract
This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.
Year
DOI
Venue
2009
10.1109/ICDE.2009.77
ICDE
Keywords
Field
DocType
search strategy,keyword search,distance mutex,the user is interested to find a spatial location that the blog is likely to be relevant to 1. this can be done by issuing an mck query on the three keywords. the measure of closeness for a set of m tuples is defined as the maximum distance between any two of the tuples:,data pruning,by document. as an example,novel spatial keyword query,fig. 1 shows the spatial distribution of three keywords that are obtained from placemarks in some mapping application. given a blog that contains these three keywords,tree data structures,visual databases,spatial object,priori-based search strategy,geotagged document searching,mck query,database indexing,m-closest spatial keyword query,spatial closest tuple,query keyword,spatial database,spatial databases,search space,br*-tree index,m-closest keywords,search by document,query response time,keyword mutex,query processing,geotagged document,r*-tree,data mining,probability density function,indexes,r tree,pediatrics,databases,indexation
Query optimization,Web search query,Data mining,R-tree,Query expansion,Information retrieval,Tuple,Computer science,Web query classification,Database index,Database,Spatial database
Conference
ISSN
ISBN
Citations 
1084-4627 E-ISBN : 978-0-7695-3545-6
978-0-7695-3545-6
143
PageRank 
References 
Authors
4.88
26
5
Search Limit
100143
Name
Order
Citations
PageRank
Dongxiang Zhang174343.89
Yeow Meng Chee259362.01
Anirban Mondal338631.29
Anthony K. H. Tung43263189.90
Masaru Kitsuregawa53188831.46