A Systematic Literature Review on Data Provenance Visualization

Abstract

Data provenance is being one of the emerging needs for the domains and technologies to grant end-user to analyse and evaluate data life-cycle. Particularly in Big Data world with the help of Internet of Things environments, data amount increases rapidly each day. With the growth of data, the metadata also overgrows on origin, process and life-cycle of data. Innovations and approaches using data provenance are required to provide better interpretation and understanding of data. Efficient data visualization addresses this need as an important instrument for making complex data open, accessible and available. Data visualization proposes significant approaches to establish an instinctive interpretation by maximizing the user’s perception. This research introduces a Systematic Literature Review (SLR) on Data Provenance Visualization for describing the studies that explicitly researched Data Provenance Visualization identifying which visualization approaches were considered mostly in the literature. A comprehensive and rigorous process is followed for giving confidence to the review study. Relevant primary studies are selected, and analysed in a systematic way. By proposing a significant systematic review to the literature, this study is being regarded as a reference aiming to examine particular researches on data provenance visualization approaches and technologies.