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Research Article Open Access
A large data is generated in different organization which is in textual format. In such data structured information is get shadowed in unstructured data. Many algorithms working on extraction of information from raw data but which is costly and not efficient and also shows impure results. Data quality is also the main issue. In existing system used annotation for query search and work on attribute suggestion which make querying feasible but annotation that use attribute value pairs require users to be more principled in their annotation efforts. Also user always has good idea in using and applying the annotations. In this we proposed new techniques that combine the working of (Collaborative Adaptive Data Sharing platform) CADS and USHER for attribute suggestion and improving data quality. In our approach we first generate CADS form and after that we evaluate real-world data sets components using USHER. This technique shows superior results compared to current approach. It improves the visibility of document and also data quality with minimum cost.
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Author(s): Anita L. Devkar, Dr. Vandana S. Inamdar