An Algorithm for Inferring Big Data Objects Correlation Using Word Net

© 2016 The Authors. The value of big data comes from its variety where data is collected from various sources. One of the key big data challenges is identifying which data objects are relevant or refer to the same logical entity across various data sources. This challenge is traditionally known as schema matching. Due to big data velocity traditional approaches to data matching can no longer be used. In this paper we present an approach for inferring data objects correlation. We present our algorithm that relies on the objects meta-data and it consults the Word Net thesaurus

Semantically Rich Materialisation Rules for Integrating Heterogeneous Databases

The need for accessing independently developed database systems using a unified or multiple global view(s) has been well recognised. This paper addresses the problem of redundancy of object retrieval in a multidatabase setting. We present the materialisation rules we have used for supporting data integration in a heterogeneous database environment. The materialisation rules are capable of directing the global query processor to combine data from different databases. Also, these rules are able to reconcile database heterogeneity that may be found due to independent database design