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1. Spatial Approximate String Search
ABSTRACT
This work deals with the approximate string search in large spatial databases. Specifically, we
investigate range queries augmented with a string similarity search predicate in both Euclidean
space and road networks. We dub this query the spatial approximate string (SAS) query. In
Euclidean space, we propose an approximate solution, the MHR-tree, which embeds min-wise
signatures into an R-tree. The min-wise signature for an index node u keeps a concise
representation of the union of q-grams from strings under the sub-tree of u. We analyze the
pruning functionality of such signatures based on the set resemblance between
the query string and the q-grams from the sub-trees of index nodes. We also discuss how to
estimate the selectivity of a SAS query in Euclidean space, for which we present a novel
adaptive algorithm to find balanced partitions using both the spatial and string information
stored in the tree. For queries on road networks, we propose a novel exact method, RSASSOL,
which significantly outperforms the baseline algorithm in practice. The RSASSOL combines
the q-gram based inverted lists and the reference nodes based pruning. Extensive experiments
on large real data sets demonstrate the efficiency and effectiveness of our approaches.
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2. Existing System
Keyword search over a large amount of data is an important operation in a wide range of
domains. Felipe et al. has recently extended its study to spatial databases, where keyword search
becomes a fundamental building block for an increasing number of real-world applications, and
proposed the IR -Tree.
A main limitation of the IR -Tree is that it only supports exact keyword search.
Problems on existing system:
1. Exact Keyword Require For Searching the Results.
Proposed System
For RSAS queries, the baseline spatial solution is based on the Dijkstra’s algorithm. Given
a query point q, the query range radius r, and a string predicate, we expand from q on the
road network using the Dijkstra algorithm until we reach the points distance r away from q
and verify the string predicate either in a post-processing step or on the intermediate results
of the expansion. We denote this approach as the Dijkstra solution. Its performance degrades
quickly when the query range enlarges and/or the data on the network increases. This
motivates us to find a novel method to avoid the unnecessary road network expansions, by
combining the prunings from both the spatial and the string predicates simultaneously.
We demonstrate the efficiency and effectiveness of our proposed methods for SAS
queries using a comprehensive experimental evaluation. For ESAS queries, our experimental
evaluation covers both synthetic and real data sets of up to 10 millions points and 6
dimensions. For RSAS queries, our evaluation is based on two large, real road network
datasets, that contain up to 175,813 nodes, 179,179 edges, and 2 millions points on the road

3. network. In both cases, our methods have significantly outperformed the respective baseline
methods.
Advantages:
This is very helpful for Exact Result from Non Exact keywords .
Main Modules:-
1. User Module:
In this module, Users are having authentication and security to access the detail
which is presented in the ontology system. Before accessing or searching the details user
should have the account in that otherwise they should register first.
.
2. key:
The key of common Index can be made from the Index word given by the Data owner
and File. The secure index and a search scheme to enable fast similarity search in the context of
data. In such a context, it is very critical not to sacrifice the confidentiality of the sensitive data
while providing functionality. We provided a rigorous security definition and proved the
security of the proposed scheme under the provided definition to ensure the confidentiality.
3. Edit Distance Pruning:
Computing edit distance exactly is a costly operation. Sev- eral techniques have been
proposed for identifying candidate strings within a small edit distance from a query string fast.
All of them are based on q-grams and a q-gram
counting argument. For a string s, its q-grams are produced by sliding a window

4. of length q over the characters of s. To deal with the special case at the beginning and the end of
s, that have fewer than q characters, one may introduce special characters, such as “#” and “$”,
which are not in S. This helps conceptually extend
s by prefixing it with q - 1 occurrences of “#” and suffixing it with q - 1 occurrences of “$”.
Hence, each q-gram for the string s has exactly q characters.
4. Search:
we provide a specific application of the proposed similarity searchable encryption
scheme to clarify its mechanism.Server performs search on the index for each component and
sends back the corresponding encrypted bit vectors it makes by the respective like commend.
Finally, we illustrated the performance of the proposed scheme with empirical analysis on a real
data.
Configuration:-
H/W System Configuration:-
Processor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA

5. S/W System Configuration:-
 Operating System :Windows95/98/2000/XP
 Application Server : Tomcat5.0/6.X
 Front End : HTML, Java, Jsp
 Scripts : JavaScript.
 Server side Script : Java Server Pages.
 Database : Mysql 5.0
Database Connectivity : JDBC.