A general framework for blockchain analytics

Bartoletti, Massimo; Lande, Stefano; Pompianu, Livio; Bracciali, Andrea

doi:10.1145/3152824.3152831

Cited by 75 publications

(46 citation statements)

References 32 publications

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“…All these studies are made possible by novel strategies to perform the blockchain representation and the clustering of Bitcoin addresses in a more efficient way [15][16][17][18]. In particular, Pinna et al [19] used a bipartite graph (represented as Petri net), to describe as nodes both entities and transactions and to allow performing investigations and statistics, and Bartoletti et al [20] proposed a general framework to deeply analyze blockchain data properly stored in a database, by using the database query language. The use of SQL databases to represent blockchain data is also proposed by Yue et al [21] and by the project Bitcoin Database Generator (https://github.…”

Section: Related Workmentioning

confidence: 99%

Applying the ETL Process to Blockchain Data. Prospect and Findings

et al. 2020

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We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction. We describe the implementation of address clustering algorithms specific for the Bitcoin blockchain and the process to collect and transform data and to load them in the database. To balance the input data rate with the elaboration time, we manage blockchain data according to the lambda architecture. To evaluate our process, we first analyzed the performances in terms of scalability, and then we checked its usability by analyzing loaded data. Finally, we present the results of a toy analysis, which provides some findings about blockchain data, focusing on a comparison between the statistics of the last year of transactions, and previous results of historical blockchain data found in the literature. The ETL process we realized to analyze blockchain data is proven to be able to perform a reliable and scalable data acquisition process, whose result makes stored data available for further analysis and business.

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Section: Related Workmentioning

confidence: 99%

Applying the ETL Process to Blockchain Data. Prospect and Findings

et al. 2020

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“…In the following, we first use a heuristic case study to illustrate the significance of the blockchain storage issue. Among many research topics emerging from the blockchain technology, Bitcoin is one of the most successful implementations of blockchain [14]. A Bitcoin block consists of a block header with a size of 80 bytes and a list of transactions as block payload (or block body) [15].…”

Section: Opportunities and Challenges To Integrate Blockchain Inmentioning

confidence: 99%

“…Although the size of the block header is small, one of the major drawbacks of the existing Bitcoin witnessing scheme is that the auditors have to download the entire Bitcoin blockchain. As of November 2018, the Bitcoin blockchain contains more than 190GB of data, and it grows by 52GB every year [16] [17] [14]. It is a challenging task if not impossible to download and store the whole blockchain in resource-constrained IoT gateways.…”

Section: Opportunities and Challenges To Integrate Blockchain Inmentioning

confidence: 99%

ChainSplitter: Towards Blockchain-Based Industrial IoT Architecture for Supporting Hierarchical Storage

Wang¹,

Shi²,

Nixon³

et al. 2019

2019 IEEE International Conference on Blockchain (Blockchain)

112

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The fast developing Industrial Internet of Things (IIoT) technologies provide a promising opportunity to build large-scale systems to connect numerous heterogeneous devices into the Internet. Most existing IIoT infrastructures are based on a centralized architecture, which is easier for management but cannot effectively support immutable and verifiable services among multiple parties. Blockchain technology provides many desired features for large-scale IIoT infrastructures, such as decentralization, trustworthiness, trackability, and immutability. This paper presents a blockchain-based IIoT architecture to support immutable and verifiable services. However, when applying blockchain technology to the IIoT infrastructure, the required storage space posts a grant challenge to resource-constrained IIoT infrastructures. To address the storage issue, this paper proposes a hierarchical blockchain storage structure, ChainSplitter. Specially, the proposed architecture features a hierarchical storage structure where the majority of the blockchain is stored in the clouds, while the most recent blocks are stored in the overlay network of the individual IIoT networks. The proposed architecture seamlessly binds local IIoT networks, the blockchain overlay network, and the cloud infrastructure together through two connectors, the blockchain connector and the cloud connector, to construct the hierarchical blockchain storage. The blockchain connector in the overlay network builds blocks in blockchain from data generated in IIoT networks, and the cloud connector resolves the blockchain synchronization issues between the overlay network and the clouds. We also provide a case study to show the efficiency of the proposed hierarchical blockchain storage in a practical Industrial IoT case.

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“…However, it does not provide support to Smart Contract platforms like Ethereum. In a subsequent similar effort, [9] proposed a framework to support data analytics on Bitcoin and Ethereum, reorganizing blockchain data in SQL or NoSQL databases. They try to address the need for combining blockchain data with external information such as user, market and crime-related data.…”

Section: Related Workmentioning

confidence: 99%

“…Focusing on the Ethereum network, [9] present a methodology to identify and collect information on Smart Contracts that implement Ponzi schemes and investigate their properties. The authors were able to identify an alarming number of Smart Contracts involved in this type of fraud, but fortunately their impact was still small.…”

Section: Related Workmentioning

confidence: 99%

Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data

Brinckman

Kuehlkamp

Nabrzyski

et al. 2019

2019 International Conference on Information and Communication Technology Convergence (ICTC)

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As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.

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A general framework for blockchain analytics

Cited by 75 publications

References 32 publications

Applying the ETL Process to Blockchain Data. Prospect and Findings

Applying the ETL Process to Blockchain Data. Prospect and Findings

ChainSplitter: Towards Blockchain-Based Industrial IoT Architecture for Supporting Hierarchical Storage

Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data

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