A Framework At Data As Service (DAAS) Layer, For Handling Interoperability And Security In Data Integration

Abstract

Data integration is a known technique to provide uniform view on sets of heterogeneous data sources. It not only provides better analysis of data but also facilitates users to query without any knowledge on the heterogeneous data sources. With the advent of Service Oriented Architecture and Cloud Computing it is possible for users to access services over the Internet along with additional advantage of having them at a low cost. One of the layers in Cloud is Data as a service (DaaS) that provides data to other services. The issue of providing an integrated view of data can be handled using Semantic data (data stored in such a way that is understandable by machines and integrate-able without human intervention). However, integrating data using semantic web technology without any access management enforced will raise privacy and confidentiality concerns. This research provides an enhancement of Data Integration. We have proposed a framework at DaaS layer, responsible for handling the issues of interoperability as well as of security in data integration from heterogeneous sources. Hence the main contribution of this research is a framework that would allow data from different sources to be integrated, thus resolving the issue of interoperability and also devise an access control system for defining explicit privacy constraints. We designed and applied our framework on both semantic and non-semantic data from heterogeneous resources. For validation of our approach, scenario based testing is done on integrated data.