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Data Mining for XML Query-Answering Support

Platform : DOT NET

Data Mining for XML Query-Answering Support

Abstract:

Extracting information from semistructured documents is a very hard task, and is going to become more and more critical as the amount of digital information available on the Internet grows. Indeed, documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this paper, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intensional information on both the structure and the contents of Extensible Markup Language (XML) documents, and can be stored in XML format as well. This mined knowledge is later used to provide: 1) a concise idea, the gist of both the structure and the content of the XML document and 2) quick, approximate answers to queries. In this paper, we focus on the second feature. A prototype system and experimental results demonstrate the effectiveness of the approach.

 

Existing System:

There are two main approaches to XML document access: keyword-based search and query-answering. The first one comes from the tradition of information retrieval, where most searches are performed on the textual content of the document; this means that no advantage is derived from the semantics conveyed by the document structure. As for query-answering, since query languages for semistructured data rely on the document structure to convey its semantics, in order for query formulation to be effective users need to know this structure in advance, which is often not the case. Frequent, dramatic outcomes of this situation are either the information overload problem, where too much data are included in the answer because the set of keywords specified for the search captures too many meanings, or the information deprivation problem, where either the use of inappropriate keywords, or the wrong formulation of the query, prevent the user from receiving the correct answer.

 

Proposed System:

As for query-answering, since query languages for semistructured data rely on the document structure to convey its semantics, in order for query formulation to be effective

Users need to know this structure in advance, which is often not the case. In fact, it is not mandatory for an XML document to have a defined schema: 50 percent of the documents on the web do not possess one. When users specify queries without knowing the document structure, they may fail to retrieve information which was there, but under a different structure. This limitation is a crucial problem which did not emerge in the context of relational database management systems. Frequent, dramatic outcomes of this situation are either the information overload problem, where too much data are included in the answer because the set of keywords specified for the search captures too many meanings, or the information deprivation problem, where either the use of inappropriate keywords, or the wrong formulation of the query, prevent the user from receiving the correct answer.

 

 

 

 

HARDWARE & SOFTWARE REQUIREMENTS:

 

HARDWARE REQUIREMENTS:

 

Processor                     :        Intel Pentium-IV or Above

Speed                          :         1.6GHz

RAM                           :         2GB

Hard Disk                   :         500GB

General                        :        Key Board, Monitor, Mouse

 

 

SOFTWARE REQUIREMENTS:

 

Operating System       :           Windows XP, 7.

Software                     :           VS .NET 2008, SQL Server Tools 2005.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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