ABSTRACT:
With the proliferation of
spatial-textual data such as location-based services and geo-tagged websites,
spatial keyword queries are ubiquitous in real life. One example of
spatial-keyword query is the so-called collective spatial keyword query (CoSKQ) which is to find for a given query consisting a query location and several query keywords a set
of objects which covers the query keywords collectively and has the smallest
cost wrt the query location. In the literature, many
different functions were proposed for defining the cost and correspondingly,
many different approaches were developed for the CoSKQ
problem. In this paper, we study the CoSKQ problem
systematically by proposing a unified cost function and a unified approach for
the CoSKQ problem (with the unified cost function).
The unified cost function includes all existing cost functions as special cases
and the unified approach solves the CoSKQ problem
with the unified cost function in a unified way. Experiments were conducted on
both real and synthetic datasets which verified our proposed approach.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Harry Kai-Ho Chan,
Cheng Long, and Raymond Chi-Wing Wong, “On Generalizing Collective Spatial
Keyword Queries”, IEEE Transactions on Knowledge and Data Engineering, 2018.