Assessment and Optimization of User Imprecise Queries in Cloud Environments

Abstract

Data size growth rate arose massively day to day due to database and internet applications. It has become a challenging task to organize those data and to provide the user a relevant data in time with correct and compatible manner. Further choice was a pervasive feature of social life that profoundly affects people. They work with assumptions that stored data represent the proper subset of real world data and make a quick decision based on imprecise knowledge in daily life for survivals which tends to get irrelevant output .Sometimes for other input, this may be exact so it should not remove instead, managed to utilize appropriately to minimize the processing time. Moreover, optimism significance relies on user satisfactions. This paper provides a vision to tackle these issues by assessing the imprecise incoming query and reutilizing for future user instead of rejecting as wrong or irrelevant. To address optimization issues in this paper, we proposed the techniques for optimizing the queries to provide customers with fast data retrieval. In our model, Query processing: A 3-step process that transforms a high-level query (MongoDB) into an equivalent and more efficient lower-level query (relational algebra). Further MEAN stack based cooperative semantic approach was deployed in cloud environments as novelty to provide solution with the level of performance significance.