4. Spatial Approximate String Search

Abstract:This work deals with the approximate string search in large spatial databases. Specically, 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 nd 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 signicantly 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.

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.

Proposed System:For RSAS queries, the baseline spatial solution is based on the Dijkstras 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 nd 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 network. In both cases, our methods have signicantly outperformed the respective baseline methods.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 Dataowner 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 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 extends by prexing it with q - 1 occurrences of # and sufxing 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.

H/W System Configuration:

Processor-Intel Core 2 Duo RAM-1 GB(min) Hard Disk-300 GB

S/W System Configuration:

Operating System:Windows-XP Application Server:Apache Tomcat 5.0/6.X Front End:HTML, Java 6.0, JSP Scripts:JavaScript. Server side Script:Java Server Pages. Database:Mysql 5.0 Database Connectivity:JDBC.