Signature-Based Indexing Method is student project developed using J2EE. A number of algorithms have been proposed for the discovery of data’s from the large database. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this project, we propose a signature-based indexing method to optimize the storage and retrieval of a relative data’s from the large database.
Signature-Based Indexing Method
Project title: A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns
Existing Systems:
- Inverted Files indexing method concentrate partial match retrieval, which are basically subset queries.
- An inverted list that stores a list of references to all occurrences of this value in the database
Proposed System:
- Focuses on supporting content-based queries of data’s from the database.
- Efficiently can be retrieved by signature file indexing method
Software Requirements:
FRONT END : J2EE (JSP)
OPERATING SYSTEM : Window’s Xp
BACK END : Sql Server 2000
Modules and Description
- Finding temporal pattern similarity
- Constructing Signature Files for temporal patterns
- Answering Content-Based Queries Using the Signature File
Module 1: In this module we are maintain the temporal pattern which are variable-length objects that cannot be represented in a k-dimensional metric space. Each pattern contains a list of states. Each pattern contains a set of state relationships.
Module 2: In this module the (content-based queries), Let D be a temporal pattern database and q be a query pattern. The four forms of content-based queries that this research supports include the following:
- Subpattern queries. Find those patterns in D that contain q.
- Superpattern queries. Find those patterns in D that are a subpattern of q.
- Equality queries. Find those patterns in D equal to q.
- K-nearest subpattern queries. Find the k most similar patterns in D to q.
Superpattern queries are useful when searching for the characteristic parts of a large pattern, while k-nearest subpattern queries limit the number of patterns generated by subpattern or superpattern queries. The temporal pattern is converted into equivalent sets.
Module 3: In this module the signature file is bit representation of query, i.e. equivalent set is converted into hash function.
Module 4: In this module the query is converted into equivalent sets and that set is converted into bit form that is searched in signature files using BSSF.