scholarly journals Sports Big Data: Management, Analysis, Applications, and Challenges

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongbo Bai ◽  
Xiaomei Bai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Rapid Growth ◽  
Growth Trend ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Rich Information ◽  
Sports Data

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

scholarly journals Outlier Detection in Big Data

2014 ◽  
pp. 1762-1771 ◽  
Author(s):  
Victoria J. Hodge
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Outlier Detection ◽  
Cost Savings ◽  
Detection Methods ◽  
Complex Data ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Time Savings

Outlier detection (or anomaly detection) is a fundamental task in data mining. Outliers are data that deviate from the norm and outlier detection is often compared to “finding a needle in a haystack.” However, the outliers may generate high value if they are found, value in terms of cost savings, improved efficiency, compute time savings, fraud reduction and failure prevention. Detection can identify faults before they escalate with potentially catastrophic consequences. Big Data refers to large, dynamic collections of data. These vast and complex data appear problematic for traditional outlier detection methods to process but, Big Data provides considerable opportunity to uncover new outliers and data relationships. This chapter highlights some of the research issues for outlier detection in Big Data and covers the solutions used and research directions taken along with an analysis of some current outlier detection approaches for Big Data applications.

Big Data management and data analysis: Applied solutions in view of the spheres of the modern economy

2021 ◽  
pp. 281-306
Author(s):  
Larisa A. Ilyina ◽  
Ekaterina V. Lyubimova ◽  
Darya A. Prosvirina ◽  
Anton N. Sunteev
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Modern Economy

Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

Team-Level Constructs

2019 ◽  
Vol 6 (1) ◽  
pp. 325-348
Author(s):  
David Chan
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Future Research ◽  
Methodological Issues ◽  
Research Directions ◽  
Substantive Content ◽  
Multilevel Research ◽  
Future Research Directions ◽  
Changes Over Time ◽  
Over Time

Studies of team-level constructs can produce new insights when researchers explicitly take into account several critical conceptual and methodological issues. This article explicates the conceptual bases for multilevel research on team constructs and discusses specific issues relating to conceptual frameworks, measurement, and data analysis. To advance programmatic research involving team-level constructs, several future research directions concerning issues of substantive content (i.e., changes in the nature of work and teams, member-team fit, linking team-level constructs to higher-level constructs) and strategic approaches (i.e., the construct's theoretical roles, dimensionality and specificity, malleability and changes over time, relationships with Big Data) are proposed.

scholarly journals Big Data Applications in Social Media Research: The Experience of Unsuccessful Data Analysis of Ukrainian Facebook

2018 ◽  
pp. 94-105
Author(s):  
A. Tokarev
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Base ◽  
Research Question ◽  
Functional Limitations ◽  
State University ◽  
High Expectations ◽  
Big Data Applications ◽  
Primary Means ◽  
Academy Of Sciences

This article showcases a detailed description of the first stage of research on the discourse of Ukrainian opinion leaders on Facebook conducted by a team of researchers representing MGIMO University, Lomonosov Moscow State University, and Institute of Economy at the Russian Academy of Sciences. Convinced that it is Facebook that serves as the primary means of communication of politicians with the population in Ukraine, the team built a data base consisting of posts written over a 10-month period by 176 profiles belonging to the representatives of Ukrainian elites, and applied machine data analysis. The research question was the following: What strategies on the conflict in Donbas are verbalized by the Ukrainian elites? The author faced three challenges and limitations of machine data processing and analysis:unsuccessful operationalization of terms; functional limitations of the Semantic Archive Platform, of which the author turned out to have unreasonably high expectations; lack of understanding of peculiarities of Big Data analysis. Nevertheless, it was the failure of this pilot research that helped raise crucial questions for further research, primarily on the criteria for shaping a data base and on formulating of the research questions for software. This experience turned to be essential for second and third stages of the research project that were completed a year and half after the project was launched. Hence the necessity to make public all the considerations on this research.

Database Systems for Big Data Storage and Retrieval

2018 ◽  
pp. 76-100 ◽  
Author(s):  
Venkat Gudivada ◽  
Amy Apon ◽  
Dhana L. Rao
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Database Systems ◽  
Query Languages ◽  
Research Issues ◽  
Storage And Retrieval ◽  
Emerging Trends ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application

Special needs of Big Data applications have ushered in several new classes of systems for data storage and retrieval. Each class targets the needs of a category of Big Data application. These systems differ greatly in their data models and system architecture, approaches used for high availability and scalability, query languages and client interfaces provided. This chapter begins with a description of the emergence of Big Data and data management requirements of Big Data applications. Several new classes of database management systems have emerged recently to address the needs of Big Data applications. NoSQL is an umbrella term used to refer to these systems. Next, a taxonomy for NoSQL systems is developed and several NoSQL systems are classified under this taxonomy. Characteristics of representative systems in each class are also discussed. The chapter concludes by indicating the emerging trends of NoSQL systems and research issues.

Integrating the Split/Analyze/Meta-Analyze (SAM) Approach and a Multilevel Framework to Advance Big Data Research in Psychology

2018 ◽  
Vol 226 (4) ◽  
pp. 274-283 ◽  
Author(s):  
Yucheng Eason Zhang ◽  
Siqi Liu ◽  
Shan Xu ◽  
Miles M. Yang ◽  
Jian Zhang
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Analytical Techniques ◽  
Future Research ◽  
Firm Level ◽  
Research Directions ◽  
Research Issues ◽  
Country Level ◽  
Multilevel Research ◽  
Future Research Directions

Abstract. Though big data research has undergone dramatic developments in recent decades, it has mainly been applied in disciplines such as computer science and business. Psychology research that applies big data to examine research issues in psychology is largely lacking. One of the major challenges regarding the use of big data in psychology is that many researchers in the field may not have sufficient knowledge of big data analytical techniques that are rooted in computer science. This paper integrates the split/analyze/meta-analyze (SAM) approach and a multilevel framework to illustrate how to use the SAM approach to address multilevel research questions with big data. Specifically, we first introduce the SAM approach and then illustrate how to implement this to integrate two big datasets at the firm level and country level. Finally, we discuss theoretical and practical implications, proposing future research directions for psychology scholars.

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