Nowadays, the technologies of distributed ledger systems are becoming more and more significant and in demand in various fields, for example: financial services, logistics, healthcare, the Internet of Things and some government functions. Their popularity is due to a number of advantages that are much overpowered to classical information systems: decentralization, security, transparency, immutability, scalability and transactional profitability, the introduction of which can lead to simplification of business processes, increased security, lower costs, guarantees of anonymity and transparency of operations. The totality of all this implies completely different approaches to data processing and storage. How can changes in the data models of distributed ledger systems affect the above advantages?

The distributed ledger systems are based on consensus algorithms [2, 3, 4], which play a key role as they are responsible for ensuring consistency and reliability of data between network participants, in other words: achieving agreement and trust by making decisions about which version of data is considered correct and how it is distributed over the network. Data models, on the other hand, define the structure and format of data that is stored in a distributed ledger system. These models define which data types are supported, how they are organized, and how they can be changed [5, 6]. It turns out that the consensus algorithm determines which data is considered correct and should be accepted by all network participants. Depending on the consensus algorithm chosen, different rules may impose restrictions on data models. For example, one consensus algorithm may require agents’ voting to change the state of the data, while another algorithm may allow one selected node to make decisions.

Data models define how data is stored and organized in a distributed ledger system. This may include defining the block structure or ways to represent transactions and account states. Consensus algorithms must take these data models into account to make sure that data changes are consistent with consensus rules and propagated correctly across the network [5]. There are cases when consensus algorithms can even influence which data model can be selected for use in a distributed ledger system. Some algorithms may be more suitable for certain data models or may have limitations on the types of data that can be used. To answer the above question, I would like to compare the classic model of the Bitcoin [4] distributed ledger system with the distributed ledger system developed for fast payments as MIOTA [1, 6]. The first is considered a classic model of a blockchain system, where transactions are formed into blocks and connected to each other in a single chain. This chain, of course, can be bifurcated for some time, but the final system will be represented by a single chain. A direct alternative is the MIOTA [2, 3] cryptocurrency, which is based on the Tangle data structure, which is a directed acyclic graph [6] using the rules of a certain consensus algorithm based on Proof Of Work [4]. The purpose of creating this cryptocurrency was to achieve the maximum possible network throughput with the least sacrifice in terms of security relative to the same Bitcoin system.

The data model for transactions played a key role in achieving this goal, as the developers decided not to limit themselves to one chain and not block the distribution of transactions in the network thereby. The concept is that each transaction is represented by its own unique block, unlike its predecessor, and the validation of transactions takes place according to the principle: “If an agent on the network has sent a transaction, then he must validate two existing ones.” Due to the fact that in this case the computing power of the validator will be significantly reduced, the developers have simplified the Proof Of Work algorithm to the minimum possible number of significant bits when calculating hashes, which allows you to validate blocks faster. The validation process itself consists in the alternate validation of the block on the way from the genesis (the very first block) to the block without validators. During the path, blocks are randomly selected relative to some weights. Thus, a directed acyclic graph [6] is formed instead of the usual chain, where edges characterize the validation process. Reducing the number of significant bits in the Proof of Work consensus algorithm on the scalability trilemma leads to a decrease in security, but this is compensated for with a sufficiently high network load due to the growth of the graph itself [3].

Also, considering all of the above, it is worth noting the validator models. In a classical distributed ledger system like Bitcoin, the validator is a separate node that is not connected in any way with the process of sending transactions, which makes the system unbalanced and can lead to unstable transaction fees for nodes sending transactions. In the case of MIOTA or Tangle, each node in the network represents both a sender and a validator, which leads to the fact that theoretically the network should scale linearly along with throughput, which guarantees minimal commissions or no commissions at all [2].

As part of the comparison, it was possible to give only two examples based on real distributed ledger systems, from which it can be concluded that the requirements for a distributed ledger system in the form of its properties determine the consensus algorithm, which in turn determines the data models. It is also worth noting that it is impossible to preserve all the advantages, often when changing the data model in favor of the requested requirement, you have to sacrifice something. Bitcoin and MIOTA are two different systems with different data models and consensus algorithms. In the scope of consensus algorithms, these are PoW and Tangle, and in the framework of transaction models, these are blocks and a directed acyclic graph. These solutions are oriented for different applications, which is due to the difference between them and their advantages.

1. Bin Cao, Zhenghui Zhang, Daquan Feng, Shengli Zhang, Lei Zhang, Mugen Peng, Yun Li Performance analysis and comparison of PoW, PoS and DAG based blockchains https://www.sciencedirect.com/science/article/pii/S2352864819301476 (accessed: 21.10.2023). ScienceDirect.

2. Serguei Popov The Tangle https://assets.ctfassets.net/r1dr6vzfxhev/2t4uxvsIqk0EUau6g2sw0g/45eae33637ca92f85dd9f 4a3a218e1ec/iota1_4_3.pdf (accessed: 21.10.2023). ARXIV. 3. Bartosz Kusmierz, William Sanders, Andreas Penzkofer, Angelo Capossele and Alon Gal Properties of the Tangle for uniform random and random walk tip selection https://arxiv.org/pdf/2001.07734.pdf (accessed: 21.10.2023). Научная электронная библиотека ARXIV.

4. Satoshi Nakamoto Bitcoin: A Peer-to-Peer Electronic Cash System https://bitcoin.org/bitcoin.pdf (accessed: 21.10.2023). BITCOIN. 5. Sirine SAYADI, Sonia BEN REJEB, Ziéd CHOUKAIR Blockchain Challenges and Security Schemes: A Survey https://www.researchgate.net/profile/Sirine-Sayadi/publication/330626077_Blockchain_Challenges_and_Security_Schemes_A_Survey/links/5c6c2d61a6fdcc404ebed9d3/Blockchain-Challenges-and-Security-Schemes-A-Survey.pdf (accessed: 21.10.2023). ResearchGate.

6. Qin Wang, Jiangshan Yu, Shiping Chen, Yang Xiang SoK: Diving into DAG-based Blockchain Systems https://arxiv.org/abs/2012.06128 (accessed: 21.10.2023). ARXIV.