scholarly journals Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection

2021 ◽  
Vol 11 (17) ◽  
pp. 7987
Author(s):  
Aida Kamišalić ◽  
Renata Kramberger ◽  
Iztok Fister
Keyword(s):  
Data Mining ◽  
Anomaly Detection ◽  
Research Area ◽  
Modern Information Technology ◽  
Research Areas ◽  
Blockchain Technology ◽  
Real World Applications ◽  
Mining Methods ◽  
Modern Information ◽  
It Revolution

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.

scholarly journals A Survey of Anomaly Detection Using Data Mining Methods for Hypertext Transfer Protocol Web Services

2015 ◽  
Vol 11 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Mohsen Kakavand ◽  
Norwati Mustapha ◽  
Aida Mustapha ◽  
Mohd Taufik Abdullah ◽  
Hamed Riahi
Keyword(s):  
Data Mining ◽  
Web Services ◽  
Anomaly Detection ◽  
Hypertext Transfer Protocol ◽  
Mining Methods ◽  
Using Data ◽  
Transfer Protocol

Data mining methods for anomaly detection KDD-2005 workshop report

2005 ◽  
Vol 7 (2) ◽  
pp. 132-136 ◽  
Author(s):  
Dragos Margineantu ◽  
Stephen Bay ◽  
Philip Chan ◽  
Terran Lane
Keyword(s):  
Data Mining ◽  
Anomaly Detection ◽  
Workshop Report ◽  
Mining Methods

scholarly journals Noval Stream Data Mining Framework under the Background of Big Data

2016 ◽  
Vol 16 (5) ◽  
pp. 69-77 ◽  
Author(s):  
Wenquan Yi ◽  
Fei Teng ◽  
Jianfeng Xu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Research Area ◽  
Stream Data ◽  
Data Set ◽  
Stream Data Mining ◽  
Big Data Applications ◽  
Clustering And Classification ◽  
Mining Methods ◽  
New Research

Abstract Stream data mining has been a hot topic for research in the data mining research area in recent years, as it has an extensive application prospect in big data ages. Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification. However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data. So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framework is proposed for big steam data mining. In this framework, online and offline model are organized for different tasks, the interior of each model is rationally organized according to different mining tasks. This framework provides a new research idea and macro perspective for stream data mining under the background of big data.

scholarly journals Actual Needs Criteria for Assessing Data Classification Platforms

2021 ◽  
Vol 3 (1) ◽  
pp. 125-138
Author(s):  
Baydaa Mohammed Merzah .
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Science ◽  
Software Metrics ◽  
Research Area ◽  
Mining Machine ◽  
Assessment Procedure ◽  
Data Set ◽  
Science Field ◽  
Research Areas

A Software tools have an important role in different research areas. Generally they provide time and efforts saving. In computer science filed these tools can help in communications, web site development, software metrics finding, data mining, machine learning and many other fields. There are many specialized tools built to support specific purpose. Users and researchers spend a lot of time and efforts to select between the large amounts of the available platforms. Each has its own characteristics, some are open source and the other licensed with trial version to test them. In this work we will focus on some platforms related to data mining research area. The selected tools represent widely used and trusted ones with most updated version. We will study platforms from different perspectives. They have different data processing features, but they support common algorithms helps us to evaluate between them. Four data mining tools and four data set were selected. The assessment procedure done from multi-points of view as we will see in the methodology section of this article. The criteria collected from a survey done among a population of researchers interested in the field of data mining and machine learning. The Contribution of this work is to assess the selected platforms depending on new actual needs criteria. These criteria give a clear idea for the researchers to determine the best platform according to their resources. The results highlighted the power for each platform. Orange and Weka show best performance over the rest. These results will be the guide for beginners or researchers out the computer science field to select the appropriate platform for their needs and available resources.

Overview and initial results of the National Center for Health Statistics’ Research and Development Survey1

2020 ◽  
Vol 36 (4) ◽  
pp. 1199-1211
Author(s):  
Jennifer Parker ◽  
Kristen Miller ◽  
Yulei He ◽  
Paul Scanlon ◽  
Bill Cai ◽  
...  
Keyword(s):  
Survey Data ◽  
Evaluation Studies ◽  
Research Area ◽  
Production Cycle ◽  
Response Patterns ◽  
Health Statistics ◽  
Mode Effects ◽  
Research Areas ◽  
Statistical Agencies ◽  
Initial Results

The National Center for Health Statistics is assessing the usefulness of recruited web panels in multiple research areas. One research area examines the use of close-ended probe questions and split-panel experiments for evaluating question-response patterns. Another research area is the development of statistical methodology to leverage the strength of national survey data to evaluate, and possibly improve, health estimates from recruited panels. Recruited web panels, with their lower cost and faster production cycle, in combination with established population health surveys, may be useful for some purposes for statistical agencies. Our initial results indicate that web survey data from a recruited panel can be used for question evaluation studies without affecting other survey content. However, the success of these data to provide estimates that align with those from large national surveys will depend on many factors, including further understanding of design features of the recruited panel (e.g. coverage and mode effects), the statistical methods and covariates used to obtain the original and adjusted weights, and the health outcomes of interest.

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