Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Top K Temporal Spatial Keyword Search   Massive amount of data that are geo-tagged and associated with text
information are being generated at an unprecedented scale in many emerging
applications such as location based services and social networks. Due to their
importance, a large body of work has focused on efficiently computing various
spatial keyword queries. In this paper,we study the top-$k$ temporal spatial
keyword query which considers three important constraints during the search
including time, spatial proximity and textual relevance. A novel index
structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web
objects with high rates. Base on SSG-tree an efficient algorithm is developed
to support top-k temporal spatial keyword query. We show via extensive
experimentation with real spatial databases that our method has increased
performance over alternate techniques

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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga