Effective Methods for Range Aggregate Queries in Big Data with Enhanced Security

Abstract Big data analysis can realize development of various societal aspects and preferences of individual day by day deeds. This provides a new prospect to explore elementary questions about the composite world. Presently, it is important to provide proficient techniques and tools for big data analysis. Efficient Range Aggregate queries are important tools in decision management, online proposition, trend estimation, and so on. Existing methods for handling range aggregate queries are insufficient to quickly obtain accurate results in big data. In this paper, we propose effective methods for handling range aggregation queries in big data. Proposed system makes use of hadoop distributed file system, which will provide framework for the analysis and transformation of very large data sets using the Map Reduce paradigm. The interface to hadoop file system will be Linux file system, which in turn improve the performance for the applications. In proposed system, Big data will get divided into independent partitions with map reduce paradigm, and then generates estimation sketch for each partition. When range aggregation query request arrives, system will obtain the result directly by summarizing estimates from all partitions. The big data involves major increase in data volumes, and the selected tuples maybe locate in different files formats i.e. data may present in structured, semi structured or unstructured format. In this paper, proposed system aims to provide fast approach for range aggregate query in order to fetch results within least amount of time by using structured and semi structured heterogeneous file context.