Geodesic
A Dockerized Big Data Architecture for Sports Analytics
Computer Science and Information Systems, Tome 19 (2022) no. 2.

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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.
Keywords: big data, sports analytics, containers, wearable devices, IoT, reproducible research 
@article{CSIS_2022_19_2_a21,
     author = {Yavuz Melih \"Ozg\"uven and Utku G\"onener and S\"uleyman Eken},
     title = {A {Dockerized} {Big} {Data} {Architecture} for {Sports} {Analytics}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {19},
     number = {2},
     year = {2022},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a21/}
}
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Yavuz Melih Özgüven; Utku Gönener; Süleyman Eken. A Dockerized Big Data Architecture for Sports Analytics. Computer Science and Information Systems, Tome 19 (2022) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a21/