Proceedings ArticleDOI
Keyword Search on Spatial Databases
I. De Felipe,Vagelis Hristidis,Naphtali Rishe +2 more
- pp 656-665
TLDR
This work presents an efficient method to answer top-k spatial keyword queries using an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R- Tree with superimposed text signatures.Abstract:
Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability.read more
Citations
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Journal ArticleDOI
Efficient retrieval of the top-k most relevant spatial web objects
TL;DR: A new indexing framework for location-aware top-k text retrieval that encompasses algorithms that utilize the proposed indexes for computing the top- k query, thus taking into account both text relevancy and location proximity to prune the search space.
Journal ArticleDOI
Spatial keyword query processing: an experimental evaluation
TL;DR: An all-around survey of 12 state-of-the-art geo-textual indices and proposes a benchmark that enables the comparison of the spatial keyword query performance, thus uncovering new insights that may guide index selection as well as further research.
Proceedings ArticleDOI
Collective spatial keyword querying
TL;DR: This paper defines the problem of retrieving a group of spatial web objects such that the group's keywords cover the query's keywords and such that objects are nearest to the query location and have the lowest inter-object distances and designs exact and approximate solutions with provable approximation bounds to the problems.
Proceedings ArticleDOI
Keyword Search in Spatial Databases: Towards Searching by Document
TL;DR: This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query, which aims to find the spatially closest tuples which match m user-specified keywords, and introduces a new index called the bR*-tree, which is an extension of the R-tree.
Journal ArticleDOI
IR-Tree: An Efficient Index for Geographic Document Search
TL;DR: An efficient index, called IR-tree, is proposed 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.
References
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Proceedings ArticleDOI
R-trees: a dynamic index structure for spatial searching
TL;DR: A dynamic index structure called an R-tree is described which meets this need, and algorithms for searching and updating it are given and it is concluded that it is useful for current database systems in spatial applications.
Book
Data Compression: The Complete Reference
TL;DR: Detailed descriptions and explanations of the most well-known and frequently used compression methods are covered in a self-contained fashion, with an accessible style and technical level for specialists and nonspecialists.
Proceedings ArticleDOI
Nearest neighbor queries
TL;DR: This paper presents an efficient branch-and-bound R-tree traversal algorithm to find the nearest neighbor object to a point, and then generalizes it to finding the k nearest neighbors.
Journal ArticleDOI
Distance browsing in spatial databases
Gísli R. Hjaltason,Hanan Samet +1 more
TL;DR: The incremental nearest neighbor algorithm significantly outperforms the existing k-nearest neighbor algorithm for distance browsing queries in a spatial database that uses the R-tree as a spatial index and it is proved informally that at any step in its execution the incremental nearest neighbors algorithm is optimal with respect to the spatial data structure that is employed.
Proceedings ArticleDOI
Optimal aggregation algorithms for middleware
TL;DR: An elegant and remarkably simple algorithm is analyzed that is optimal in a much stronger sense than FA, and is essentially optimal, not just for some monotone aggregation functions, but for all of them, and not just in a high-probability sense, but over every database.