Journal ArticleDOI
Efficient retrieval of the top-k most relevant spatial web objects
Gao Cong,Christian S. Jensen,Dingming Wu +2 more
- Vol. 2, Iss: 1, pp 337-348
TLDR
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.Abstract:
The conventional Internet is acquiring a geo-spatial dimension. Web documents are being geo-tagged, and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables a new kind of top-k query that takes into account both location proximity and text relevancy. To our knowledge, only naive techniques exist that are capable of computing a general web information retrieval query while also taking location into account.This paper proposes a new indexing framework for location-aware top-k text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within the framework. The framework 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. Results of empirical studies with an implementation of the framework demonstrate that the paper's proposal offers scalability and is capable of excellent performance.read more
Citations
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Journal ArticleDOI
Spatial-Keyword Skyline Publish/Subscribe Query Processing Over Distributed Sliding Window Streaming Data
TL;DR: In this article , a distributed skyline query processing framework is proposed to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results by introducing MF-R -tree, which is an update-efficient and space-saving indexing structure.
Book ChapterDOI
User-Contributed Relevance and Nearest Neighbor Queries
TL;DR: A query processing method for so-called k-Relevant Nearest Neighbor k-RNN queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant as judged by the crowd POIs.
Proceedings ArticleDOI
A ranking measure for top-k moving object trajectories search
Vikram Goyal,Shamkant B. Navathe +1 more
TL;DR: An experimental study is done on a real dataset to demonstrate that the proposed ranking measure is indeed effective in terms of retrieval of trajectories that have query activities at places near to the query locations.
Journal ArticleDOI
A survey on location based serach using spatial inverted index method
N. Minojini,D. Daniel,S. Jothi +2 more
TL;DR: A variant of inverted index is employed that's effective for multidimensional points associate degreed come with an Rtree which is constructed on each inverted list, and uses the algorithm of minimum bounding methodology which will answer the closest neighbor queries with keywords in real time.
Proceedings ArticleDOI
Time-Constrained Indoor Keyword-aware Routing
TL;DR: Wang et al. as discussed by the authors proposed a new problem formulation TIKRQ that considers the time needed for a user to complete the route, in addition to other criteria such as static cost and textual relevance.
References
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Book
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TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
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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.
Proceedings ArticleDOI
The R*-tree: an efficient and robust access method for points and rectangles
TL;DR: The R*-tree is designed which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory which clearly outperforms the existing R-tree variants.