# 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. distributed ledger technology (DLT); blockchain; block explorer; hyperledger fabric; abstraction layer; information visualization; analytics +
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visualizations. This can be achieved by employing an abstraction layer, which aggregates data from the ledger, pre-processes it and provides higher level APIs to block explorers and analytics dashboards that can then intuitively present information readily. In data analytics, visual representations are primarily designed to make sense of data and provide insights. They usually model data structures, which help to expand the boundary of individuals' cognitive system [14]. Not only do visual representations support users' reasoning, but also provide a construct to manipulate information. They can be used to both structure information and reduce individuals' cognitive burden by easing external anchoring, information foraging, and cognitive offloading [15]. In DLTs, audits are often conducted through lists and tables of low-level block data that do not easily allow for tracking and tracing digital assets [11]. The introduction of adequate visual representations of the ledgers' data has the capacity to enable a higher level of functionalities, and therefore improve the intuitiveness of the auditing processes. A 2019 survey showed that time series representation accounted for 53% of all visualization techniques used for DLT representation, followed by basic charts such as bar charts, pie charts and histograms, which accounted for 41%, while tree and graph visualizations represented 38% [16]. The main reason behind this distribution can be explained by the time domain nature of the blockchain components, which contain time-stamped information. However, to represent data within more specific contexts, higher level types of visualizations were not used as frequently. For instance, map-based visualizations and other multi-dimensional representations accounted for only 13% and 7% of all visualization types, respectively. To build intuitive visualizations or conduct an in-depth analysis of blockchain's recorded operations, data must be readily available in a format that can be consumed by the frontend visual representations. Although most DLTs provide access to the ledger via their software development kits (SDKs), they only offer low-level query interfaces that often lack semantic richness or functionality. For instance, information about transactions, contracts or blocks can usually be queried by their corresponding hashes. Such basic query interfaces are not adequate to map out high-level information or derive visualizations from blockchains' ledgers, and hence make it significantly challenging for users to deduce valuable insights that can serve complex business analysis quickly. As a result, a few block explorers in the literature and marketplace implement high-level ledger visualizations or analysis functionalities.