An Approach to Standalone Provenance Systems for Big Social Provenance Data

Abstract

Provenance about data derivations in social networks is usually called social data provenance, which helps in the assessment of data quality, resource tracking, and understanding the dissemination of information in social networks. The collection and processing of social data provenance leads to some challenges such as scalability, data quality, and privacy awareness. This study introduces a test suite to evaluate the current state-of-the-art standalone and centralized provenance systems. We conduct performance (responsiveness) and scalability experiments and investigate whether the standalone provenance systems are capable of handling large-size social provenance data. We also propose a software architecture for a decentralized and scalable provenance management system for big social provenance data.