# Abstract
These limitations drastically reduce the possibility to adapt the abstraction of the displayed information to the targeted audience and application. The level of abstraction needs careful consideration when designing a visualization system to avoid situations where the users are presented with either insufficient or too much low-level data. In visualization and analytics, mismatched levels of granularity can result in both less accurate comprehension of a situation and higher time to complete a given task [17]. Figure 1: Transaction handling in different ledger architectures [10]. (**a**) Conventional centralized ledger. (**b**) Decentralized ledger. Curating information and presenting users with notable insights at the right level of granularity is often the responsibility of data analysts and designers. However, in the relatively new DLTs landscape, there are no such easily accessible solutions to implement the appropriate blockchain visualization tools to address a target audience's needs in a specific scenario [17]. For instance, firms of the regulated sectors willing to address compliance and trust issues might engage in decentralized digital assets trading by means of DLTs [18]. By design, DTLs encourage each participant to operate several peers on the network. For an effective and intuitive auditing process, visualization and analytics tools would need to represent the data at a participant-level, in an abstract and high-level manner. This requires aggregating the data from all the peers operated by a same participant. However, current off-the-shelf solutions typically provide information at a peer-level and are therefore limited to low-level representation of the ledgers' data [19]. The lack of high-level abstractions make the development of visual analytics time consuming for the developers since new functionalities need to be designed and implemented to analyze and aggregate the available low-level data. Additionally, delegating such functionalities to frontend applications can result in high resource-consuming services for the devices rendering such information. Therefore, there is a clear need for a standardized intermediate-level abstraction that provides higher level information from the low-level block data [19]. This article proposes an abstraction layer framework that facilitates better design of business analytics for DLT-based systems and the decentralized applications (DApps) that run on them. The purpose of this work is to establish a higher level of abstraction that improves the auditability and intuitiveness of distributed ledger records and enables further development of future user interfaces including analytics and visualization tools. Based on the analysis of existing initiatives and identification of the relevant user requirements, we infer specifications to improve the auditability and usability of block explorers. An abstraction layer has been designed to bridge the gap between a DLT and a user interface. As a result, the proposed abstraction layer coupled with a new user interface (UI) promotes the ease of analytics such as tracking and tracing of the history of operations, clustering of user addresses, and labeling of entities. Finally, to illustrate the benefits offered by the proposed abstraction layer architecture, we explore an industrial case study based on the RegNet platform [20]. RegNet is an infrastructure for trusted data access that removes the need to explicitly share data through the use of federated learning and tokenization. Its goal is to provide access to analytics in an auditable and privacy preserving manner, while being able to comply with data policies such as General Data Protection Regulation (GDPR) [21]. The remainder of this paper is organized as follows. We firstly review the related work by highlighting notable aspects in existing visualization tools, block explorers and abstraction layer implementations. Secondly, we propose an approach to build account and transaction-oriented abstractions while addressing auditability and intuitiveness issues.