International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 5 - Apr 2014
An Efficient and Enhanced approach for Spatial Search results
Sarojini *, Satyanarayana Mummana#
M.Tech Scholar*,Assistant Professor#
Department of CSE, Avanthi Institute of Engineering & Technology, Visakhapatnam. Andhra Pradesh
Abstract: Optimizing the search engine is still an
interesting research issue in the field of search engine
optimization, Even though various approaches
available for answering the spatial query, modern
technology requires some enhancements for the
optimality, 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 in two ways and returns the only user
specified number of optimal results and we
implemented a cache based approach for efficient
results.
I.INTRODUCTION
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.
Spatial information includes the geographic
information, which includes the parameters like latitude
and longitude it represents the global position of any
location in the world .Distance can be calculated by the
computing the distance measures like Euclidean or
Manhattan distances. Usually spatial query includes the
object type and attribute set, attribute set includes the
collection of properties with respect to the object.
Computation cost parameter is the basic parameter while
searching the information over the search engines and
sometimes nearest neighbor information is the interesting
factor while searching the spatial information.
ISSN: 2231-5381
Spatial data objects can be gathered based on the distance
measures between the user search query and query results
by retrieving the geo codings of the user.Caching is the
technique which stores the frequently access information
instead of access the previously accessed information again
and again, that obviously increases the user performance
and reduces the server processing work.
Even though various approaches available for the
cache implementation of spatial queries those are not
optimal to resolve the user search query in efficient manner
Conventional spatial databases manage objects located on a
thematic map with 100% certainty and In real-life cases,
however there may be uncertainty about the existence of
spatial objects or events and consider an example a satellite
image where interesting objects (e.g., vessels) have been
extracted . Due to low resolution of image and/or color
definitions and the data extractor may not be 100% certain
about whether a pixel formation corresponds to an actual
object x; a probability Ex could be assigned to x, reflecting
the confidence of x’s existence. We call such objects
existentially uncertain, since uncertainty does not refer to
their locations, but to their existence. As another example
of existentially uncertain data, consider emergency calls to
a police calling center, which are dialed from various map
locations.
Depending on various factors (e.g., crime-rate of
the caller’s district, caller’s voice, operator’s experience,
etc.), for each call we can generate a spatial event
associated with a potential emergency and a probability
that the emergency is actual. Existential probabilities are
also a natural way to model fuzzy classification In this
case, the class label of a particular object is uncertain; each
class label takes an existential probability and the sum of
all probabilities is 1.
We can naturally define probabilistic versions of
spatial queries that apply on collections of existentially
uncertain objects and We identify two types of such
probabilistic geographical queries and Given a confidence
threshold value and a thresholding query returns the objects
or object pairs while in case of a join which qualify some
spatial predicates with probability at least t. E.g., given a
segmented satellite image with uncertain objects, consider
a port officer who wishes to find a set of vessels S such
that every x 2 S is the nearest ship to the port with
confidence at least 305 and which have high confidence. A
ranking spatial query returns the objects which qualify the
spatial predicates of the query, in order of their confidence.
Ranking queries can also be threshold (in analogy to
nearest neighbor queries) by a parameter m and For
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 5 - Apr 2014
instance the proposed port officer may want to retrieve the
m = 10 ships with the highest probability to be the nearest
neighbor of the port.
In this paper are proposing a cache based
approach for handling the queries jointly with
corresponding geocoding parameters.
II.RELATED WORK
the fuzzy representations. In addition, they provide a
methodology that sets the maximum precision error given a
desired guaranteed uncertainty of the query results. [12]
studies the evaluation of spatial joins between two sets of
objects, for the case where the object extents are ‘floating’
according to uncertainty distance bounds. An extension of
the R–tree that captures uncertainty in directory node
entries is proposed. Both the filter and refinement steps of
RJ are then adapted to process the join efficiently.
Recently, there is an increasing interest on the
modeling, indexing, and querying of objects with uncertain
location mutual extent, consider a collection of moving
objects, whose positions are tracked by GPS devices. Exact
locations are unknown due to GPS errors and transmission
delays and consider an example as if the object is in motion
its location might be outdated when reaching the listening
server and locations are approximated by probability
density functions (PDFs), which integrate GPS error ranges
and known moving object velocities. For instance, the
uncertainty of a location can be modeled by
a 2-dimensional Gaussian function, centered at the
coordinates tracked from the GPS.
We are proposing an empirical model of mechanism for
location based (Spatial) databases, Even though 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 in two ways and returns the only user specified
number of optimal results, we implemented a cache based
approach for efficient results.
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 highestscoring 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.
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 querynode 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
There are other models the uncertain locations of spatial
objects by (circular) uncertainty regions and discuss how to
process simple and aggregate spatial range queries using
ISSN: 2231-5381
Procedure:
If Query available in cache
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 5 - Apr 2014
Result
related
ForwardToTreeprocess (Query)
to
query:
=
For Each child in tree
If(status==true)
Else
Getnodebyfeature (Qi);
Result related to query: = GeocodingtreeProcess
(Query)
Getnodebyfeature (Qj);
End
Geocoding process(Query):
Else
Parameters
Empty list ()
Qi—Input Spatial Query
Qj (j=1…n) ---Set of Queries contains same Location
End For Each
Dist[j] (j=1…..n)-----Array for set of distances
4.Add nodes to list
5.Return list
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
Step By Step process:
1. User provides the spatial query which involves the
spatial object and feature
2. if data not available in cache
Server retrieves the Object oriented results,
Ex:((Object, Feature)
If Q.features[i]==Q.features[j]
Else
Add to list
Retrieves the Optimal results from the cache
End while
Sort list
by feature and distance
3. filter the feature oriented results from the Object
oriented results
Return list.
4. Retrieve the Geo codings of the end user and calculate
the Euclidean distance between the end user geocodings
the filtered results
ForwardToTreeprocess ()
5. Sort the results based on Euclidean distance
1. Build an empty list
6. Return the sorted distance wise spatial results to the end
user
2 .Make a root node
3. if Qi in cache and status=false
For j=0 to n
Compare features(Qi,Qj) status=true;
ISSN: 2231-5381
IV.CONCLUSION
Finally we proposed an efficient a novel search
implementation on spatial databases with simple
implementation than the complex tree constructions like R
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International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 5 - Apr 2014
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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ISSN: 2231-5381
Bibliography:
Satyanarayana Mummana
is
working as an Asst. Professor in
Avanthi Institute of Engineering &
Technology, Visakhapatnam, Andhra
Pradesh. He has received his Masters
degree (MCA) from Gandhi Institute of
Technology
and
Management
(GITAM), Visakhapatnam and M.Tech (CSE) from
Avanthi Institute of Engineering & Technology,
Visakhapatnam. Andhra Pradesh. His research areas
include Image Processing, Computer Networks, Data
Mining, Distributed Systems, Cloud Computing
http://www.ijettjournal.org
Sarojini Devi completed her Btech
in Avanthi Institute of Engineering
& Technology, Visakhapatnam,
Andhra Pradesh,currently pursuing
MTech in
Avanthi Institute of
Engineering
&
Technology,
Visakhapatnam
Her research
intrests are Data mining and Data
warehousing and network security
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