Abstract: Databases nowadays contain large volumes of data, and they are accessed by numerous users on a daily basis. The large volume of data poses challenges to both users accessing the databases and the companies or organizations managing them. Enterprises on the other hand, need to make their content visible and accessible to the users and identify which objects in their database (e.g. products) have a significant impact on the user basis and use this information for promoting their products. The original target of increasing the visibility of the available products is thus hindered by the abundance of products contained in the database. It is therefore necessary to develop data exploration techniques that will enable users to explore large databases and provide them with a wide, yet coherent overview of objects that fit their preferences. In this paper, we propose exploratory algorithms that return to the user a small number of results, which at the same time provide a wide overview of the available content. We also propose analysis techniques are KNN algorithm used for best keyword search over spatial database(location based service), AES algorithm is implemented for secure accessing of users privacy data’s i.e..location information and Hilbert Curve algorithm used for cache maintenance, caching scheme that aims to reduce query-response times and network traffic between the clients and the server by attempting to answer queries locally from the cached tuples using associated predicate descriptions. Identifying frequent search objects that are attractive to the users. This algorithm more efficient algorithm that achieves results of comparable quality, but with significantly lower processing cost.
Keywords: Keyword Search, KNN, AES, HilbertCurve.