Best Keyword Cover Search Using Distance and Rating

Mr. Vishal D. Kolekar D. Kolekar, Prof. Ajay Kumar Gupta


Spatial databases are stores the information about
the spatial objects which are associated with the keywords to
indicate the information such as its business/services/fea-tures.
Very important problem known as closest keywords search
is to query objects, called keyword cover. In closest keyword
search, it covers a set of query keywords and minimum distance
between objects. From last few years, keyword rating increases
its availability and importance in object evaluation for the
decision making. This is the main reason for developing this
new algorithm called Best keyword cover which is considers
inter-distance as well as the rating provided by the customers
through the online business review sites. Closest keyword search
algorithm combines the objects from different query keywords
to generate candidate keyword covers. Baseline algorithm and
keyword nearest neighbors expansion algorithms are used to
find the best keyword cover. The performance of the closest
keyword algorithm drops dramatically, when the number of
query keyword increases. To solve this problem of the existing
algorithm, this work proposes generic version called keyword
nearest neighbor expansion which reduces the resulted candidate
keyword covers.

Full Text:



Ke Deng, Xin Li, Jiaheng Lu et al. Best keyword cover search. IEEE

Transactions on Knowledge and Data Engineering. 2015; 27(1).

X. Cao, G. Cong, C. Jensen. Retrieving top-k prestige-based relevant

spatial web objects. Proc. VLDB Endowment. 2010; 3(1/2): 373384p.

X. Cao, G. Cong, C. Jensen et al. Collective spatial keyword querying.

In Proc. ACM SIGMOD Int. Conf. Manage. Data. 2011; 373384p.

G. Cong, C. Jensen, D. Wu. Efficient retrieval of the top-k most relevant

spatial web objects. Proc. VLDB Endowment. 2009; 2(1): 337348p.

I. D. Felipe, V. Hristidis, N. Rishe. Keyword search on spatial databases.

In Proc. IEEE 24th Int. Conf. Data Eng. 2008; 656665p.

R. Hariharan, B. Hore, C. Li et al. Processing spatial keyword (SK)

queries in ge-ographic information retrieval (GIR) systems. In Proc. 19th

Int. Conf. Sci. Statist. Database Manage. 2007; 1623p.

Z. Li, K. C. Lee, B. Zheng et al. IRTree: An efficient index for geographic

docu-ment search. IEEE Trans. Knowl. Data Eng. 2010; 99(4): 585599p.

J. Rocha-Junior, O. Gkorgkas, S. Jonassen et al. Efficient processing

of top-k spa-tial keyword queries. In Proc.12th Int. Conf. Adv. Spatial

Temporal Databases. 2011; 205222p.

S. B. Roy, K. Chakrabarti. Location-aware type ahead search on spatial

databases: Semantics and efficiency. In Proc. ACM SIGMOD Int. Conf.

Manage. Data. 2011; 361372p.

D. Zhang, Y. Chee, A. Mondal et al. Keyword search in spatial databases:

Towards searching by document. In Proc. IEEE Int. Conf. Data Eng. 2009;


N. Mamoulis and D. Papadias, ”Multiway spatial joins,” ACM Trans.

Database Syst., vol. 26, no. 4, pp. 424-475, 2001.

D. Papadias, N. Mamoulis, and B. Delis, ”Algorithms for querying by

spatial structure,” in Proc. Int. Conf. Very Large Data Bases, 1998, pp.

D. Papadias, N. Mamoulis, and Y. Theodoridis, ”Processing and optimization of multiway spatial joins using r-trees,” in Proc. 18th ACM

SIGMOD-SIGACTSIGART Symp. Principles Database Syst., 1999, pp.


J. M. Ponte and W. B. Croft, ”A language modeling approach to

information retrieval,” in Proc. 21st Annu. Int. ACM SIGIR Conf.

J. Rocha-Junior, O. Gkorgkas, S. Jonassen, and K. Norva g, ”Efficient

processing of top-k spatial keyword queries, in Proc. 12th Int. Conf.

Adv. Spatial Temporal Databases, 2011, pp. 205-222. Collaborative

computing,”INFOCOM 2008, pp. 1211-1219


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