Overview of Applied Data Analytic Mechanisms and Approaches Using Permissioned Blockchains

Abstract

Blockchain technology deployment has surged in diverse domains to secure and maintain valuable data. Wherever valuable data exists, the motivation of applying analytics emerges. However, this case is slightly different since it deals with a distributed system environment with security constraints such as privacy and confidentiality. This study aims to provide an overview of approaches that applied analytics over permissioned blockchains. Moreover, extract key features from these studies to report and discuss common features and best practices. This contributes to determining the requirements to apply analytics and outlines the remaining challenges. The research method was conducted in four phases. The initial phase states the goals and objectives. Subsequently, the analysis phase examines a group of research papers to extract key features from various studies. These features were divided into three categories: general aspects, data management, and an analytics perspective. Afterward, the outcomes are classified according to the findings and observations to point out common aspects and best practices. Finally, the evaluation of the research determines the requirements to apply data analytics over permissioned blockchains. Based on the findings and observations of these research papers. Most of the studies focused on off-chain analytics with the assistance of a third party. Also, most of the analytics types were descriptive and diagnostic, whereas fewer studies proposed predictive analytics. This explains the lack of existing approaches that use artificial intelligence and real-time analysis. The most used blockchain platform for analytics was Hyperledger fabric for multiple reasons mentioned in detail in this research.

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