International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
A Novel Geo-coding and Cache Based Approaches
for Spatial Queries
R.Subbarao1, K.Srikanth2
1
1,2
M.Tech Scholar,2 Associate Professor
Dept of CSE, Pydah College of Engineering and Technology
Visakhapatnam
Abstract: This paper proposes an efficient algorithm for
spatial databases, various approaches are delivered by the
various researchers for finding the result based on the
keywords, and usually spatial query is a combination of a
location and set of features. In our approach we are handling
the spatial queries jointly and returns the only user specified
number of optimal results, we implemented a cache based
approach for efficient results.
the nearest neighbors and for each neighbor an inverted
index is used to check if the query keywords are contained.
We show that such two-phase approaches are inefficient.
In this paper are proposing a cache based approach
for handling the queries jointly with corresponding
geocoding parameters.
I. INTRODUCTION
In a nutshell, given a user query consisting of several
keywords, a Standard search engine ranks the pages in its
collection in terms of .Their relevance to the keywords.
This is done by using a text index Structure called an
inverted index to retrieve the IDs of pages containing the
keywords, and then evaluating a term-based ranking
Function on these pages to determine the k highest-scoring
pages. (Other factors such as hyperlink structure and user
behavior are Also often used, as discussed later). Query
processing is highly optimized To exploit the properties of
inverted index structures, stored In an optimized
compressed format, fetched from disk using efficient Scan
operations, and cached in main memory.
Let D be a dataset in which each object p ∈ D is a
pair (λ, ψ) of a spatial location p.λ and a textual description
p.ψ (e.g., the facilities and menu of a restaurant). Similarly,
a spatial keyword query [3] q = _λ, ψ_ has two
components, where q.λ is a spatial location and q.ψ is a set
of keywords. The answer to query q is a list of k objects
that are in ascending order of their distance to the query
location q.λ and whose descriptions contain the set of query
keywords q.ψ Formally, let the function dist( ・ , ・ )
denotes the Euclidean distance between its argument
locations, and let D(q.ψ) = {p ∈ D | q.ψ ⊆ p.ψ} be the
objects in D that contain all the keywords in q. The result
of the top-k spatial keyword query q, q(D), is a subset of
D(q.ψ) containing k objects such that ∀p ∈ q(D) (∀p_ ∈
D(q.ψ)−q(D) (dist(q.λ, p.λ) ≤ dist(q.λ, p_.λ))). The joint
top-k spatial keyword query Q is a set {qi} of such queries.
We introduce the following notion to capture useful
information on a joint query Q: (i) Q.λ = MBRqi∈Q qi.λ is
the minimum bounding rectangle (MBR) of the locations
of the subqueries in Q, (ii) Q.ψ = ∪qi∈Q qi.ψ is the union
of the keyword sets of the subqueries in Q, and (iii) Q.m =
minqi∈Q |qi.ψ| is the smallest keyword set size of a
The World-Wide Web has reached a size where it is
becoming increasingly challenging to satisfy certain
information needs. While search engines are still able to
index a reasonable subset of the (surface) web, the pages a
user is really looking for are often buried under hundreds
of thousands of less interesting results. Thus, search engine
users are in danger of drowning in information. Adding
additional
terms to standard keyword searches often fails to narrow
down results in the desired direction. A natural approach is
to add advanced features that allow users to express other
constraints or preferences in an intuitive manner, resulting
in the desired documents to be returned among the first
results. In fact, search engines have added a variety of such
features, often under a special advanced search interface,
but mostly limited to fairly simple conditions on domain,
link structure, or modification date.
A spatial keyword query consists of a query area and a
set of keywords shown in below figure. The answer is a list
of objects ranked according to a combination of their
distance to the query area and the relevance of their text
description to the query keywords. A simple yet popular
variant, which is used in our running example, is the
distance-first spatial keyword query, where objects are
ranked by distance and keywords are applied as a
conjunctive filter to eliminate objects that do not contain
them.
Unfortunately there is no efficient support for topk spatial keyword queries, where a prefix of the results list
is required.
Instead, current systems use ad-hoc combinations of
nearest neighbor (NN) and keyword search techniques to
tackle the problem. For instance, an R-Tree is used to find
ISSN: 2231-5381
II. RELATED WORK
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
subquery in Q. We later define a variable qi.τ that captures
the upper bound kth nearest neighbor distance of subquery
qi. The value Q.τ = maxqi∈Q qi.τ then represents the
maximum upper bound kth nearest neighbor distance of all
the subqueries in Q. 3Referring to Figure 1, the joint query
Q contains three subqueries q1, q2, and q3 (shown as
shaded dots). The objects (e.g., restaurants) are shown as
white dots. We have that: q1.ψ = {curry,sushi}, q2.ψ =
{seafood, sushi}, q3.ψ = {curry, seafood}. Note that Q.λ
denotes the MBR of the shaded dots in the figure. We also
have: Q.ψ = {curry, seafood, sushi} and Q.m = 2.
Figure1: A Dataset of Spatial Keyword Objects
III. PROPOSED SYSTEM
In this paper we proposed an efficient approach for
handling the spatial query based on geocodings(latitude
and longitudes) and for optimal performance we introduced
Cache mechanism. we implemented both approaches with
cache and without cache. Our entire process is divided in to
two phases based on cache and geocodes. Geocoding
process receives the query and find and the geocodings of
the respective user from where he made a query and get the
geocoding values from the all the queries from the tree.
Find the distance with all the query nodes and input query
node and compare all features of the query node with all
node features until it meets the leaf node and add the
results to the list frequently and returns the result
Input: Query, Cache Queries
Output: Result set generated for query
Procedure:
If Query available in cache
Result
related
to
query:
=
ForwardToTreeprocess (Query)
Else
Result related to query: = GeocodingtreeProcess
(Query)
Geocoding process(Query):
Parameters
ISSN: 2231-5381
Qi—Input Spatial Query
Qj (j=1…n) ---Set of Queries contains same Location
Dist[j] (j=1…..n)-----Array for set of distances
Procedure:
(xi,yi)---Geocodings of Qi
(xj,yj)--- Geocodings of all queries with respect to
location
Dist[i]=Euclidean distance between the geocodes
While not leafnode
Read nodes from tree For Q.features
If Q.features[i]==Q.features[j]
Add to list
End while
Sort list by feature and distance
Return list.
ForwardToTreeprocess ()
1. Build an empty list
2 .Make a root node
3. if Qi in cache and status=false
For j=0 to n
Compare features(Qi,Qj) status=true;
For Each child in tree
If(status==true)
Getnodebyfeature (Qi);
Getnodebyfeature (Qj);
End
Else
Empty list ()
End For Each
4.Add nodes to list
5.Return list
IV. CONCLUSION
Finally we proposed an efficient a novel search
implementation on spatial databases with simple
implementation than the complex tree constructions like R
trees, in both cache based and non cache based(with
geocodings),our algorithms shows an optimal results than
the traditional approaches.
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BIOGRAPHIES
Mr.K.Srikanth completed his M.Tech
at Gitam University.Visakhapatnam in
the
year 2008 . He is having 15
years of experience in teaching to
engineering .Currently he is working as
Associate Professor , department of CSE at Pydah college
of engineering and technology. JNTUK University. His
areas of interest include
Computer Organization,
Computer Networks, Operating
Systems , Software
Engineering , Advanced Information Systems
Mr.R.Subbarao completed his B.tech at
Sarada Institute of Technology and
Sciences in 2010. He is pursuing
M.Tech in Computer Science and
Engineering from Pydah college of
Engineering and Technology . JNTUK
University .His areas of interest include
C Programming, JAVA , C++.
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