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A new approach called Web Supported Query Taxonomy Classifier is introduced in this paper, which generates better searching results. We combine WSQ, Web Supported Queries approach with Query Classification in which bridging classifier and category selection method is used for classification of queries. In this paper, Query categorization will build bridging classifier in an offline mode at mediator taxonomy integrated with the category selection method for the effectiveness and efficiency of online classification and then classifier is used for mapping input queries to target taxonomy in online mode. After this the queries are settled in virtual tables presented by WSQ tuples for the generation of web search result calling one or more search engines for improved results. Further a technique called asynchronous iteration is used for concurrency among multiple web search requests and query processing. KeywordsWeb Query Classification, Asynchronous Iteration, Bridging Classifier, Category Selection Method, WSQ, Web Supported Query Taxonomy Classifier. INTRODUCTIONNowadays information is available on single click. Internet acts as a tool which contains immense information. Web users input their required data and search engines search those queries. A number of processing phases are included in a very basic search engine, it includes, Indexing, Crawling, Query Processing, and Ranking. Indexing: to build an index to facilitate query processing, crawling: to discover the web pages on the internet, Query Processing: To Extract (based on user's query terms) the most relevant page and Ranking: Based on relevancy order the result.Real time information is maintained by the search engines based on algorithm running on a web crawler. A web crawler is an agenda for computer searching. It's a computer program that browse the world wide web in an automated or systematic way. Many search engines use web crawling as a way of providing up-to-date data. A copy of all the visited pages is created for later processing by a search engine that will make a directory for the downloaded pages to provide fast searches.Search engines have gained popularity over the World Wide Web. Information moves between the World Wide Web which is considered un-structured data and database which is structured data. Figure 1 shows the relationship of structured and unstructured data which provides vast amount of information. Figure 1 Information SplitSignificant deficiencies with respect to flexibility, precision and robustness are found over web search techniques. They take queries as input because these user queries are important medium by which a system can understand user's interest. These queries are assorted and significant which requires classifying them into small commercial taxonomy. They are short so they are vague; therefore they lack precise semantic description.Query classification is an application for giving better search pages result and WSQ arrange and manage data between structured and unstructured information available on intern...
A new approach called Web Supported Query Taxonomy Classifier is introduced in this paper, which generates better searching results. We combine WSQ, Web Supported Queries approach with Query Classification in which bridging classifier and category selection method is used for classification of queries. In this paper, Query categorization will build bridging classifier in an offline mode at mediator taxonomy integrated with the category selection method for the effectiveness and efficiency of online classification and then classifier is used for mapping input queries to target taxonomy in online mode. After this the queries are settled in virtual tables presented by WSQ tuples for the generation of web search result calling one or more search engines for improved results. Further a technique called asynchronous iteration is used for concurrency among multiple web search requests and query processing. KeywordsWeb Query Classification, Asynchronous Iteration, Bridging Classifier, Category Selection Method, WSQ, Web Supported Query Taxonomy Classifier. INTRODUCTIONNowadays information is available on single click. Internet acts as a tool which contains immense information. Web users input their required data and search engines search those queries. A number of processing phases are included in a very basic search engine, it includes, Indexing, Crawling, Query Processing, and Ranking. Indexing: to build an index to facilitate query processing, crawling: to discover the web pages on the internet, Query Processing: To Extract (based on user's query terms) the most relevant page and Ranking: Based on relevancy order the result.Real time information is maintained by the search engines based on algorithm running on a web crawler. A web crawler is an agenda for computer searching. It's a computer program that browse the world wide web in an automated or systematic way. Many search engines use web crawling as a way of providing up-to-date data. A copy of all the visited pages is created for later processing by a search engine that will make a directory for the downloaded pages to provide fast searches.Search engines have gained popularity over the World Wide Web. Information moves between the World Wide Web which is considered un-structured data and database which is structured data. Figure 1 shows the relationship of structured and unstructured data which provides vast amount of information. Figure 1 Information SplitSignificant deficiencies with respect to flexibility, precision and robustness are found over web search techniques. They take queries as input because these user queries are important medium by which a system can understand user's interest. These queries are assorted and significant which requires classifying them into small commercial taxonomy. They are short so they are vague; therefore they lack precise semantic description.Query classification is an application for giving better search pages result and WSQ arrange and manage data between structured and unstructured information available on intern...
The aim of this paper is to depict new approach called taxonomy based data marts which add a new layer for the categorization of the queries using data warehouse which is the database that contains data relevant to an organization information and respond quickly to multi dimensional analytical queries. The new algorithm is introduced here for more precise results and time saving consumption using data marts which collects data for specific set of users or knowledge workers. Data warehouses often adopt a three-tier architecture. The bottom tier is a warehouse database server, which is typically a relational database system. The middle tier is an OLAP server, and the top tier is a client that contains query and reporting tools. Another new layer is added for faster results. This is done with the help of query classification technique.
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