scholarly journals A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

2020 ◽  
Vol 10 (9) ◽  
pp. 3013
Author(s):  
Alen Rajšp ◽  
Iztok Fister
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Intelligent Data Analysis ◽  
Way Of Life ◽  
Sport Training ◽  
Analysis Methods ◽  
The Internet Of Things ◽  
Data Analysis Methods ◽  
The Way

The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

Automatic Intelligent Data Analysis

2009 ◽  
pp. 1-17
Author(s):  
Martin Spott ◽  
Detlef Nauck
Keyword(s):  
Data Analysis ◽  
Knowledge Base ◽  
Expert Knowledge ◽  
Soft Constraints ◽  
Intelligent Data Analysis ◽  
Automatic Data ◽  
Analysis Methods ◽  
Automatic Data Analysis ◽  
Data Analysis Methods ◽  
Fuzzy Knowledge

This chapter introduces a new way of using soft constraints for selecting data analysis methods that match certain user requirements. It presents a software platform for automatic data analysis that uses a fuzzy knowledge base for automatically selecting and executing data analysis methods. In order to support business users in running data analysis projects the analytical process must be automated as much as possible. The authors argue that previous approaches based on the formalisation of analytical processes were less successful because selecting and running analytical methods is very much an experience-led heuristic process. The authors show that a system based on a fuzzy knowledge base that stores heuristic expert knowledge about data analysis can successfully lead to automatic intelligent data analysis.

Quantitative Data Analysis for Information Science Professionals

2020 ◽  
pp. 349-372
Author(s):  
Regis Chireshe
Keyword(s):  
Data Analysis ◽  
Literature Review ◽  
Quantitative Data ◽  
Information Science ◽  
Analysis Software ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Quantitative Data Analysis ◽  
General Aspects ◽  
Data Analysis Methods

The chapter presents general aspects of quantitative data analysis as they relate to information sciences. The chapter is based on a literature review. It begins with explaining the meaning of data and quantitative data. Kinds of quantitative data are presented. The meaning of data analysis and the reasons for data analysis are also discussed. Reasons for quantitative data analysis are also discussed. The ‘what' and ‘why' of statistics in general and for information science researchers in particular is also presented. The chapter also presents the main issues of quantitative data analysis. Steps in quantitative data analysis are also presented. Preparation of quantitative data analysis is followed by a presentation on quantitative data analysis methods. The chapter highlights the popular quantitative data analysis software. A brief presentation on how quantitative data are presented and interpreted is given. The chapter ends with a discussion on the advantages and disadvantages of quantitative data analysis.

ENSEMBLE MODELS FOR INTELLIGENT ANALYSIS OF SOCIOLOGICAL DATA

2021 ◽  
pp. 43-52
Author(s):  
A. G. Podvesovskii ◽  
E. V. Karpenko ◽  
D. G. Lagerev ◽  
A. N. Baburin
Keyword(s):  
Data Analysis ◽  
Clustering Algorithms ◽  
Statistical Processing ◽  
Intelligent Data Analysis ◽  
Analysis Methods ◽  
Intelligent Analysis ◽  
Clustering And Classification ◽  
Analysis Models ◽  
Classification Structure ◽  
Data Analysis Methods

The paper investigates an approach to sociological information processing based on the use of intelligent data analysis methods applied to the task of processing the results of a questionnaire survey. The advantages of intelligent analysis of sociological data in comparison with traditional statistical processing are discussed, as well as the implementation features and applicability limits of various intelligent data analysis methods in solving problems of association, clustering and classification. Structure and features of representation of respondents’ survey data are considered, the appropriateness is substantiated and the advantages of their processing based on the combination of various methods of intelligent analysis within an ensemble of models are discussed. A structure of an ensemble of models is proposed based on the combination and joint use of association rules, clustering algorithms and decision trees, which makes it possible to jointly process numerical and categorical data contained in the respondent’s answers to the questionnaire and also to interpret the results of data clustering. The paper describes the results of using the constructed ensemble of models for processing and analyzing the data of a sociological survey conducted as part of the annual project for monitoring the drug abuse situation in the Bryansk region in 2013 – 2018. The use of an ensemble of intelligent data analysis models for processing the results of a sociological survey not only makes it possible to detect patterns in them that cannot be otherwise detected by traditional methods of statistical processing, but also contributes to an increase in the reliability, completeness and coherence of the analysis results, due to which the analyst creates a holistic systemic picture of the studied social phenomenon or process.

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