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-Preliminary interpretation supports exploratory search in large databases. The user interacts with it by specifying probability distributions over attributes, which then expresses imprecise conditions about the entities of interest. Preliminary interpretation helps the user on the right query conditions by addressing three key challenges: 1) Efficiently computing results for an imprecise query. 2) Gives out the result of the sensitivity value of individual and combines Queries. 3) Suggesting ideas for respective attributes for the user. Keywords: Interactive data exploration and discovery, Probability Query, Imprecise Queries, Sensitivity Analysis. I. INTRODUCTIONThe main notion of preliminary interpretation using ambiguous queries is helping the user with imprecise queries. The existing system is not able to handle the uncertainty of ambiguous queries, whereas proposed system allows user to express the uncertainty through the probabilistic value. We propose this project, a new approach for exploratory searching large databases. This project provides a novel method to interactively compose imprecise database queries with probabilistic conditions, while providing constant feedback to the user about the most likely results and the potential benefit and risk of each condition. This method is designed to accommodate uncertainty and imprecision in user-provided query conditions through two major technical contributions: A novel notion of sensitivity to quantify the impact of uncertainty on the query result. Fast algorithms for calibrated probability estimation that can adapt to a user-specified real-time constraint on system response time. To illustrate the need for imprecise queries with probabilistic conditions, consider the following example motivated by collaboration with the Cornell Lab of Ornithology. Through hugely successful citizen science projects such as the Lab has collected more than 100 million reports of bird sightings, adding tens of millions annually. It wants to leverage this resource to help less experienced birders identify the species of a bird they observed. Assume each observation in the database specifies properties of the bird (e.g., species, size, color) and the observation event (e.g., location, weather, habitat, and features).

Abstract-Growth in the information technology is playing an increasing role in use of information systems having relational databases. These relational databases are used very effectively in the computer environments for information extraction. They are exposed to security threats concerned by the ownership rights. Watermarking is used to enforce ownership rights about the shared relational data and for providing a means of tackling data tampering. When ownership rights are applied using watermarking, the underlined data undergoes certain modifications, as a result of which the data quality gets no attention. The reversible watermarking technique is employed to ensure data quality along with the recovery of the database. Such techniques are usually not robust against attacks and do not provide any method to select and apply a watermark on a particular attribute by taking into the picture its part in knowledge discovery. Hence the reversible watermarking is required that ensures the watermark encoding and decoding by accounting for the act of all the features in knowledge discovery. A robust and reversible watermarking (RRW) technique for the numbers relational data has been proposed that corresponds to the above objectives. Researches prove the effectiveness of reversible and robust watermarked technique against malicious attacks and shows that the proposed technique outperforms present ones.