A Graph based Data Integration and Aggregation Technique for Big Data

Abstract

Data integration is a vital issue in the conditions of heterogeneous data sources. As of now, the in advance of referenced heterogeneity is getting boundless. In view of different data sources, in the event that we need to acquire valuable information and knowledge, we should take care of data integration issues to apply suitable insightful techniques to extensive and uniform data. All the more especially, we propose a novel engineering for instance matching that considers the particularities of this heterogeneous and conveyed setting. Rather than expecting that instances share a similar schema, the proposed technique works in any event, when there is no cover between schema, aside from a key name that matching instances should share. In addition, we have thought about the conveyed idea of the Semantic Web to propose another design for general data integration. The arrangement consolidates ETL innovation and a wrapper layer known from intervened frameworks. It additionally gives semantic integration through association component between data components. The outcomes accomplished in this work are especially intriguing for the Semantic Web and Data Integration people group.