A Survey on Techniques to Extract High- Quality Data from the Database

Abstract: Search data using queries on different type of databases like biomedical, web, domain specific databases,etc., often return a large number of results (hundreds and thousands), out of which only a small set of them are relevant to the user. By combing Ranking and Categorization search the data from the database can reduce overload. The efficiency of the results fetched by the user can be improved by enhancing trustworthy. Presently, few trust factors are being considered to provide quality data to the fetched queries. A new approach for evaluating the trust factors recommendation, user expertise, topic and incentive, provenance, decency is presented to enhance trustworthiness in this truth finder algorithm to provide trust value and quality assessment policies. Quality assessment will done to assess the quality assessment policy, provides better quality. The trustworthiness can be improved and it will provide higher quality and efficient results from the database