Data Mining and Blockchain

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (25 April 2022) | Viewed by 10560

Special Issue Editors


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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor 2000, Slovenia
Interests: advanced data technologies; blockchain technology; data mining; big data; decision support; knowledge-based systems

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Guest Editor
Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000 Maribor, Slovenia
Interests: computational social science; data mining; sport science
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Special Issue Information

Dear Colleagues,

In recent years, Blockchain, together with other distributed ledger technologies (DLTs), has been gaining special attention from both academia and industry. Different industries, such as banking, e-commerce, insurance, supply chain, education, and healthcare, are discovering the great potential of Blockchain technology that can improve efficiency, process automation, sharing control over data, secure data sharing, etc. On the other hand, data mining is a process of discovering knowledge from a vast amount of data, while detecting patterns and finding anomalies and relationships in data. It is essential to investigate how data mining, analysis, and intelligence might be integrated into the underlying frameworks of current and future applications that benefit from the unique properties of Blockchain and DLTs.

This Special Issue focuses on data mining approaches in analyzing Blockchain data. The aim of this Special Issue is to compile the recent advances in this contemporary research area.

Potential topics include but are not limited to the following:
- Association rule mining in Blockchain;
- Analyses of different ledgers;
- Data analytics on Blockchain;
- Cryptocurrency price prediction;
- Discovering anomalies in distributed ledgers;
- Network science and Blockchain;
- Practical applications of DLTs;
- Security and privacy aspects of Blockchain;
- Smart contracts;
- Machine learning and DLTs;
- Coins analytics;
- Social network analysis.

Dr. Aida Kamisalic
Dr. Iztok Fister Jr.
Guest Editors

Manuscript Submission Information

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Keywords

  • Association rule mining
  • Blockchain
  • Data mining
  • Machine learning
  • Distributed ledger technologies
  • Evolutionary computation
  • Network science

Published Papers (2 papers)

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Research

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18 pages, 650 KiB  
Article
Privacy-Preserving Data Mining on Blockchain-Based WSNs
by Niki Hrovatin, Aleksandar Tošić, Michael Mrissa and Branko Kavšek
Appl. Sci. 2022, 12(11), 5646; https://0-doi-org.brum.beds.ac.uk/10.3390/app12115646 - 02 Jun 2022
Cited by 5 | Viewed by 1743
Abstract
Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and [...] Read more.
Currently, the computational power present in the sensors forming a wireless sensor network (WSN) allows for implementing most of the data processing and analysis directly on the sensors in a decentralized way. This shift in paradigm introduces a shift in the privacy and security problems that need to be addressed. While a decentralized implementation avoids the single point of failure problem that typically applies to centralized approaches, it is subject to other threats, such as external monitoring, and new challenges, such as the complexity of providing decentralized implementations for data mining algorithms. In this paper, we present a solution for privacy-aware distributed data mining on wireless sensor networks. Our solution uses a permissioned blockchain to avoid a single point of failure in the system. Contracts are used to construct an onion-like structure encompassing the Hoeffding trees and a route. The onion-routed query conceals the network identity of the sensors from external adversaries, and obfuscates the actual computation to hide it from internally compromised nodes. We validate our solution on a use case related to an air quality-monitoring sensor network. We compare the quality of our model against traditional models to support the feasibility and viability of the solution. Full article
(This article belongs to the Special Issue Data Mining and Blockchain)
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Review

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37 pages, 657 KiB  
Review
Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
by Aida Kamišalić, Renata Kramberger and Iztok Fister, Jr.
Appl. Sci. 2021, 11(17), 7987; https://0-doi-org.brum.beds.ac.uk/10.3390/app11177987 - 29 Aug 2021
Cited by 20 | Viewed by 6603
Abstract
Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT [...] Read more.
Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data mining has already been present in IT for many decades. Data stored in a blockchain can also be considered to be big data, whereas data mining methods can be applied to extract knowledge hidden in the blockchain. In a nutshell, this paper presents the interplay of these two research areas. In this paper, we surveyed approaches for the data mining of blockchain data, yet show several real-world applications. Special attention was paid to anomaly detection and fraud detection, which were identified as the most prolific applications of applying data mining methods on blockchain data. The paper concludes with challenges for future investigations of this research area. Full article
(This article belongs to the Special Issue Data Mining and Blockchain)
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