Live Data Stream Classification for Reducing Query Processing Time: Design and Analysis

The problem of data analysis and making decisions are increases with the volume of data. In other words processing of large data requires large resources to process and providing the final response. The big data is environment which is used for the large data processing and their analytics. But when the traffic is high and block size of data is larger than the query response is generated with the significant amount of delay. In order to optimize the delayed response need to make some effort for improving the performance of the big data systems. In this paper we proposed a new approach for solving this delayed data response based on streamed data mining. The proposed approach contributes for demonstration of the live twitter stream gathering, pre-processing of data and transformation of the unstructured data into the structured data features, classification of data streams using the group learning concept for streamed text data. This approach improves the query processing time and produces response in less time even when a single pattern is appeared for the query processing.