Machine Learning on Blockchain Data: A Systematic Mapping Study
Georgios Palaiokrassas Sarah Bouraga Leandros Tassiulas
Context: 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).
  Objective: 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.
  Method: 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.
  Results: 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.
  Conclusion: 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.