Query processing on road networks has many
applications. For solving the spatial range queries the baseline
solution is based on the Dijkstra’s algorithm. The earlier works
provide efficient methods for exact keyword search only. But the
RSASSOL algorithm returns the best objects that approximately
match both the spatial predicate and string predicate. The OPRN
algorithm retrieves points that have shortest distance to the
query point and have textual similarity to the query keywords.
Several string similarity functions are used here. The
unnecessary points are pruned away in each stage. The results of
extensive experiments show that this algorithm yields a flexible
framework for the efficient processing of range queries.
Published In : IJCAT Journal Volume 1, Issue 4
Date of Publication : 31 May 2014
Pages : 137 - 140
Figures : 03
Tables : --
Publication Link : A Review on String Queries on Road Networks
Sherlin Susan George : Received the bachelor’s degree in Computer
Science and Engineering from Mahatma Gandhi University, Kerala in
2012. Presently she is pursuing her Mtech in the department of
Computer Science and Engineering from Cochin University of
Science and Technology, Kerala. Her research interests include Data
Mining.
Remya R : Received the bachelor’s degree in Information Technology
from University college Trivandrum, Kerala in 2004 and master’s
degree in Computer Science and Engineering from Anna University,
Coimbatore in 2008.Currently working as an Assistant Professor in
Information Technology department, College of Engineering
Perumon, under Cochin university of Science and Technology. She
has teaching experience of eight years. Research interests Includes
Data Mining.
This work presents a comprehensive study for spatial
approximate string queries in road networks. We use the
edit distance, cosine similarity as the similarity
measurement for the string predicate and focus on the
range queries as the spatial predicate. .Given a query, the
Dijikstras, RSASSOL, OPRN algorithms on road network
returns the best objects with shortest path to the query
location and textual relevance to the query keyword.
[1] Tang, and Mario Hadjieleftheriou “Spatial
Approximate String Search” Proc. IEEE Int’l Conf.
Knowledge and Data Engineering vol .5. No.6. June
2013.
[2] Joao B.Rocha-Junior and Kjetil Norvag,”Top-k-
Spatial Keyword Queries on Road Networks” EDBT
26–30, Berlin, Germany 2012.
[3] Xin Caoy Gao Congy Christian S. Jensen “Retrieving
Top k Prestige Based Relevant Spatial Web Objects”,
Proceedings of the VLDB Endowment, Vol. 3, No.
1,2010.
[4] D. Felipe, V.Hristidis, and N. Rishe. “Keyword
Search on Spatial Databases” Proc.IEEE Int’l Conf.
Data Eng.(ICDE), pp.656-665,2008.
[5] Oystein Egeland Carlsson “Keyword Search on
Spatial Network Databases”.This research was
supported in part by NSF grants CNS-0320956, CNS-
0220562, HRD-0317692, and IIS-0534530,2004.
[6] S.Alsubaiee, A. Behm, and C. Li, “Supporting
Location-Based Approximate-Keyword Queries,”
Proc. SIGSPATIAL 18th Int’l Conf. Advances in
Geographic Information Systems (GIS), pp. 61 -70,
2010.
[7] Ron Gutman “Reach-based Routing: A New Approach
to Shortest Path Algorithms Optimized for Road
Networks”, proceedings of the sixth workshop on
algorithm engineering and experiments,New
York,2010
