Abstracting Data in Distributed Ledger Systems for Higher Level Analytics and Visualizations

Abstract

By design, distributed ledger technologies persist low-level data, which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide an enriched level of analytics. The ability to derive richer analytics could be improved through the availability of a higher level abstraction of the data. This article proposes an abstraction layer architecture that enables the design of high-level analytics of distributed ledger systems and the decentralized applications that run on top. Based on the analysis of existing initiatives and identification of the relevant user requirements, this work aims to establish key insights and specifications to improve the auditability and intuitiveness of distributed ledger systems by leveraging the development of future user interfaces. To illustrate the benefits offered by the proposed abstraction layer architecture, a regulated sector use case is explored.

A Novel Authentication Scheme Based on Verifiable Credentials Using Digital Identity in the Context of Web 3.0

“…In Ref. [116], to highlight the potential of blockchain data for perceptive analysis and decision-making, Vinceslas, Dogan, Sundareshwar, and Kondoz concentrate on abstracting data in distributed ledger systems for higher-level analytics and visualizations.…”

Special Issue on Security and Privacy in Blockchains and the IoT Volume II

“…Current blockchain analytics tools address this challenge by using an internal middleware that maps high-level user queries to low-level data interfaces. To improve the analytics capabilities of such tools, Vinceslas et al [12] introduce an abstraction layer that provides blockchain data in aggregated and pre-processed form to block explorers and other analytics dashboards. The work aims to improve the auditability and intuitiveness of DLTs by providing users with lightweight data interfaces such as dashboards.…”