A Dockerized big data architecture for sports analytics-Reference-Cited by-同舟云学术

A Dockerized big data architecture for sports analytics

Published:2022 Issue:2 Volume:19 Page:957-978
ISSN:1820-0214
Container-title:Computer Science and Information Systems
language:en
Short-container-title:COMSIS J

Author:

Özgüven Yavuz¹,Gönener Utku²,Eken Süleyman³

Affiliation:

1. Kocaeli University, Department of Computer Engineering, Izmit, Turkey

2. Kocaeli University, Faculty of Sports Sciences, Izmit, Turkey

3. Kocaeli University, Department of Information Systems Engineering Izmit, Turkey

Abstract

The big data revolution has had an impact on sports analytics as well. Many large corporations have begun to see the financial benefits of integrating sports analytics with big data. When we rely on central processing systems to aggregate and analyze large amounts of sport data from many sources, we compromise the accuracy and timeliness of the data. As a response to these issues, distributed systems come to the rescue, and the MapReduce paradigm holds promise for largescale data analytics. We describe a big data architecture based on Docker containers with Apache Spark in this paper. We evaluate the architecture on four data-intensive case studies in sport analytics including structured analysis, streaming, machine learning approaches, and graph-based analysis.

Publisher

National Library of Serbia

Subject

General Computer Science

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