[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118178-en":3,"doc-seo-118178-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118178,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning on Blockchain Data - A Systematic Mapping Study","Blockchain technology has become a prominent subject in both research and practice, producing large, publicly accessible datasets. The study provides a comprehensive, systematic mapping review of machine learning methods applied to blockchain data, aiming to identify, analyze, and classify existing literature and highlight areas needing further investigation. A structured process selects 159 articles and organizes them by use case, blockchain, data characteristics, and learning models. Results show dominant attention to anomaly use cases, Bitcoin-focused work, large datasets, and classification as the most applied ML task.","arXiv :2403 . 17081v1 [ cs .CR] 25 Mar 2024  \nMachine Learning on Blockchain Data: A Systematic Mapping Study  \nGeorgios Palaiokrassas 1,2 , Sarah Bouraga3 , Leandros Tassiulas 1,2  \n1Yale Institute for Network Science, Yale University, USA  \n2 Department of Electrical Engineering, Yale University, USA  \n3 Namur Digital Institute (NADI), Belgium  \n{georgios.palaiokrassas, [leandros.tassiulas](leandros.tassiulas}@yale.edu)[}](leandros.tassiulas}@yale.edu)[@yale.edu](leandros.tassiulas}@yale.edu),  \n[sarah.bouraga@unamur.be](sarah.bouraga@unamur.be)  \nAbstract  \nContext: Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for Machine Learning (ML) .  \nObjective: The objective of this paper is to provide a comprehensive review of the state of the art on machine learning applied to blockchain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research.  \nMethod: A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 159 articles were selected and classified according to various dimensions, specifically, the domain use case, the blockchain, the data, and the machine learning models.  \nResults: The majority of the papers (49.7%) fall within the Anomaly use case. Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset consisting of more than 1.000.000 data points was used by 31.4% of the papers. And Classification (46.5%) was the ML task most applied to blockchain data.  \nConclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel machine learning algorithms, the lack of a standardization framework, blockchain scalability issues and cross-chain interactions as areas worth exploring in the future.  \nKeywords: Blockchain, Machine learning, Systematic mapping study  \n1 Introduction  \nBlockchain technology has sparked a lot of interest over the years. A particularly interesting characteristic of the technology is the transparency it offers. Indeed, all the transactions recorded on a public blockchain (amounting to hundreds of thousands of transactions a day, just for Bitcoin) can be viewed, retrieved and analyzed by anyone. This is a huge paradigm shift, compared to incumbent institutions such as traditional banks.  \nThe amount of available blockchain data offers a lot of potential for analysis. We can analyze the blockchain data to discover unknown patterns in the data, or to predict the next cryptocurrency price, or to detect fraud to name a few. In order to carry out these types of analyses, we can use machine learning, a subfield of Artificial Intelligence. Since machine learning requires a lot of data to perform well, and since blockchain data are public and available in large quantities, this seems like a match made in heaven.  \nThis claim is supported by the plethora of scientific articles we analyze here. Many researchers addressed the questions raised above, i.e. they tried to discover hidden patterns in blockchain data, they proposed solutions for the prediction of cryptocurrency prices, others focused on the detection of fraudulent activity on a blockchain, and multiple other use cases.  \nDue to the rapid evolution of both technologies (blockchain and machine learning), it is not trivial to keep track of the state of the art: What has been done? How? On which platform?... We believe it is essential for researchers and practitioners to have a clear view of the current state of the art. On the one hand, practitioners need to know the new solutions for a given problem, t","cbCaitgwaHXiMkIl","https://ap.wps.com/l/cbCaitgwaHXiMkIl","pdf",865825,1,45,"English","en",105,"# Introduction\n## Motivation and problem scope\n# Methodology\n## Systematic mapping procedure\n# Results\n## Literature distribution by use case and platform\n## Dataset size and ML task coverage\n# Discussion\n## Research gaps and future directions","[{\"question\":\"What is the main objective of the systematic mapping study?\",\"answer\":\"To comprehensively review the state of the art on machine learning applied to blockchain data by systematically identifying, analyzing, and classifying the literature and deriving future research directions.\"},{\"question\":\"How many papers were selected and how were they organized?\",\"answer\":\"The study selected 159 articles and classified them across dimensions including domain use case, blockchain, data characteristics, and machine learning models.\"},{\"question\":\"Which use case, blockchain, dataset size, and ML task dominate the results?\",\"answer\":\"Anomaly use cases account for 49.7% of the papers, Bitcoin attracts the most attention (47.2%), 31.4% use datasets with over 1,000,000 data points, and classification is the most applied ML task (46.5%).\"},{\"question\":\"What open challenges and gaps are identified for future research?\",\"answer\":\"The study highlights opportunities such as novel machine learning algorithms, lack of a standardization framework, blockchain scalability issues, and cross-chain interactions.\"}]","Machine Learning on Blockchain Data - 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