A Dockerized Big Data Architecture for Sports Analytics

Ozguven Y. M., Gönener U., Eken S.

COMPUTER SCIENCE AND INFORMATION SYSTEMS, cilt.19, sa.2, ss.957-978, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.2298/csis220118010o
  • Dergi Adı: COMPUTER SCIENCE AND INFORMATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.957-978
  • Kocaeli Üniversitesi Adresli: Evet

Özet

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.