A knowledge-based stream processing using big data analytics

Abstract

Big Data available in almost all departments of every organization spread throughout the globe in a huge volume and category. The issues such as data heterogeneity and advanced processing capabilities are provided solution with the proposed system. Data heterogeneity is tackled by using the automatic schema mapping in the proposed work which is knowledge based solution. Inventive processing is achieved using ontology extraction and semantic inference in the proposed work. The solution is evaluated in terms of its performance and effectiveness with the publish/subscribe paradigm. The state of art analysis of huge volume of variety of data and sensory information is the real complexity. The Advanced Message Queuing Protocol is used in the proposed work for the state of art substance explanation of flooding IoT data to have dynamic mingling. The proposed work gives way to produce huge amount of data that can affect the working of the smart city systems that uses IoT data. To point out the reliability and summarization of the data, information model is used in the proposed work. The data size and average exchanged message time are the measures used to examine the working of the framework. A detailed assessment of the various sensors is carried out to inspect the storage data volume and computational cost for the substance explanation of the framework.