References
- 1. Al Nuaimi, E., Al Neyadi, H., Mohamed, N., Al-Jaroodi, J. (2015), “Applications of big data to smart cities”, Journal of Internet Services and Applications, Vol. 6 No. 1, pp. 25.
- 2. Al-Mallah, M. H., Keteyian, S. J., Brawner, C. A., Whelton, S., Blaha, M. J. Rationale (2014), “Design of the Henry Ford ExercIse Testing Project (The FIT Project)”, Clinical Cardiology, Vol. 37 No. 8, pp. 456-461.10.1002/clc.22302
- 3. Borne, K. (2021), “Top 10 List – The V’s of Big Data. Data Science Central – a Community for big data practitioners”, available at https://www.datasciencecentral.com/profiles/blogs/top-10-list-the-v-s-of-big-data (8 March 2021)
- 4. Che, D., Safran, M., Peng, Z. (2013), “From big data to big data mining: challenges, issue, and opportunities”, Database Systems for Advanced Applications, Vol. 19 No. 2, pp. 1-15.10.1007/978-3-642-40270-8_1
- 5. Chen, M., Mao, S., Liu, Y. (2014), “Big data: A survey”, Mobile Networks and Application, Vol. 19 No. 2, pp. 171-209.10.1007/s11036-013-0489-0
- 6. Cheng, Y., Song, Y. (2021) “Sports big data analysis based on cloud platform and its impact on sports economy”, Mathematical Problems in Engineering, Vol. 21 No. 2, pp. 1-12.10.1155/2021/6610000
- 7. De Mauro, A., Greco, M., Grimaldi, M. (2015), “What is big data? A consensual definition and a review of key research topics”, AIP Conference Proceedings, Vol. 1644 No. 1, pp. 97-104.10.1063/1.4907823
- 8. De Mauro, A., Greco, M., Grimaldi, M. (2016), “A formal definition of big data based on its essential features”, Library Review, Vol. 63 No. 1, pp. 122-135.10.1108/LR-06-2015-0061
- 9. Dijcks, J. (2012), “Oracle: Big data for the enterprise”, Redwood Shores, USA, pp. 1–16.
- 10. Emig, T., Peltonen, J. (2020), “Human running performance from real-world big data”, Nature communications, Vol. 11 No. 1, pp. 4936-4945.10.1038/s41467-020-18737-6
- 11. Favaretto, M., De Clercq, E., Schneble, C. O., Elger, B. S., Fischer, F. (2020), “What is your definition of Big Data? Researcher’s understanding of the phenomenon of the decade”, PLoS One, Vol. 15 No. 2, pp. 1-20.10.1371/journal.pone.0228987
- 12. Goel, R., Garcia, L. M. T., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., Brage, S., Bhalla, K., Srinivasan, M. (2018), “Estimating city-level travel patterns using street imagery: A case study of using Google Street View in Britain”, PLoS One, Vol. 13 No. 5, pp. 1-22.10.1371/journal.pone.0196521
- 13. Hayano, J., Kisohara, M., Yoshida, Y., Sakano, H., Yuda, E. (2019), “Association of heart rate variability with regional difference in senility death ratio: ALLSTAR big data analysis”, SAGE Open Medicine, Vol. 7 No. 2, pp. 1-7.10.1177/2050312119852259
- 14. Hou, X., Jiang, J. (2017), “Analysis on the mental quality and performance of table tennis players based on the cloud computing big data”, Technical Bulletin, Vol. 55 No. 1, pp. 348-354.
- 15. Kaur, G., Jagdev, G. (2020), “Analyzing and Exploring the Impact of Big Data Analytics in Sports Science”, in Indo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN, pp. 218-224.10.1109/Indo-TaiwanICAN48429.2020.9181320
- 16. Khan, N., Yaqoob, I., Targio Hashem, I. A., Inayat, Z., Mahmud Ali, W. K., Alam, M., Shiraz, M., Gani, A. (2014), “Big Data: Survey, Technologies, Opportunities, and challenges”, The Scientific World Journal, Vol. 2014 No. 2, pp. 1-18.10.1155/2014/712826
- 17. Kharabian Masouleh, S., Beyer, F., Lampe, L., Loeffler, M., Luck, T., Riedel-Heller, S., Schroeter, M. L., Stumvoll, M., Villringer, A., Witte, A. V. (2018), “Gray matter structural networks are associated with cardiovascular risk factors in healthy older adults”, Journal of Cerebral Blood Flow & Metabolism, Vol. 38 No. 2, pp. 360-372.10.1177/0271678X17729111
- 18. Khazaeli, M., El Kari, C. (2016), “The effects of technology and big data in sports industry”, in Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016-2020, pp. 2404-2409.
- 19. Kim, S. W., Lee, K., Sohn, J. S., Cha, S. W. (2020), “Product development using online customer reviews: A case study of the South Korean subcompact sport utility vehicles market”, Applied Sciences, Vol. 10 No. 19, pp. 1-12.10.3390/app10196918
- 20. Kokkotis, C., Moustakidis, S., Giakas, G., Tsaopoulos, D. (2020), “Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients”, Applied Sciences, Vol. 10 No. 19, pp. 1-23.10.3390/app10196797
- 21. Laney, D. (2012), “3D Data Management: Controlling Data Volume, Velocity and Variety”, available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (15 March 2021)
- 22. Li, J., Deng, K., Huanh, X., Xu, J. (2019), “Analysis and application of location-aware big complex network data”, Complexity, Vol. 2019 No. 1, pp. 1-2.10.1155/2019/3410262
- 23. Liu, G., Luo, Y., Schulte, O., Kharrat, T. (2020), “Deep soccer analytics: learning an action-value function for evaluating soccer players”, Data Mining and Knowledge Discovery, Vol. 34 No. 2, pp. 1531-1559.10.1007/s10618-020-00705-9
- 24. Liu, H. (2019), “Opportunities, challenges and Countermeasures for the development of China’s sports industry in the era of big data”, Journal of Physic: Conference Series, Vol. 1237 No. 2, pp. 1-14.10.1088/1742-6596/1237/2/022012
- 25. Marker, A. M., Steele, R. G., Noser, A. E. (2017), “Physical activity and health-related quality of life in children and adolescents: A systematic review and meta-analysis”, Health Psychology, Vol. 37 No. 1, pp. 893-903.
- 26. Matheson, G. O., Klügl, M., Engebretsen, L., Bendiksen, F., Blair, S. N., Börjesson, M., Budgett, R., Derman, W., Erdener, U., Ioannidis, J. P. A., Khan, K. M., Martinez, R., van Mechelen, W., Mountjoy, M., Sallis, R. E., Schwellnus, M., Shultz, R., Soligard, T., Steffen, K., Sundberg, C. J., Weiler, R., Ljungqvist, A. (2013), “Prevention and Management of Noncommunicable Disease”, Clinical Journal of Sport Medicine, Vol. 23 No. 2, pp. 419-429.10.1097/JSM.0000000000000038
- 27. Mobertz, L. (2019), “The Four V” s of Big Data”, available at https://www.bigdataframework.org/four-vs-of-big-data/ (24 March 2021)
- 28. Morgulev, E., Azar, O. H., Lidor, R. (2018), “Sports analytics and the big-data era”, International Journal of Data Science and Analitics, Vol. 5 No. 4, pp. 156-159.10.1007/s41060-017-0093-7
- 29. Nguyen Quynh C., Brunisholz, K. D., Yu, W., McCullough, M., Hanson, H., Litchman, M. L., Li, F., Wan, Y., VanDerslice, J. A., Wen, M., Smith, K. R. (2017), “Twitter-derived neighborhood characteristics associated with obesity and diabetes˝, Scientific Report, Vol. 7 No. 1, pp. 1-10.
- 30. Oguntimilehin A., Ademola E. O. (2014), “A Review of Big Data Management, Benefits and Challenges”, A Review of Big Data Management, Benefits and Challenges, Vol. 5 No. 6, pp. 433-438.
- 31. Owais, S. S., Hussein, N. S. (2016), “Extract Five Categories CPIVW from the 9V “s Characteristics of the Big Data”, International Journal of Advanced Computer Science and Applications, Vol. 7 No. 3, pp. 254-258.
- 32. Pappalardo, L., Cintia, P., Rossi, A., Massucco, E. (2019), “A public data set of spatio-temporal match events in soccer competitions”, Scientific Data, Vol. 6 No. 1, pp. 1-15.10.1038/s41597-019-0247-7
- 33. Park, S. U., Ahn, H., Dong, K., So, W. (2020), “Big Data Analysis of Sports and Physical Activities among Korean Adolescents”, International Journal of Environmental Research and Public Health, Vol. 17 No. 15, pp. 5577-5589.10.3390/ijerph17155577
- 34. Patel, D., Shah, D., Shah, M. (2020), “The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports”, Annals of Data Science, Vol. 7 No. 1, pp. 1-16.10.1007/s40745-019-00239-y
- 35. Phan, L., Yu, W., Keralis, J. M., Mukhija, K., Dwivedi, P., Brunisholz, K. D., Javanmardi, M., Tasdizen, T., Nguyen, Q. C. (2020), “Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States”, International Journal of Environmental Research and Public Health, Vol. 17 No. 10, pp. 3659-3669.10.3390/ijerph17103659
- 36. Rajeshwari Sreenivasan, R. (2017), “Characteristics of Big Data – A Delphi study”, Newfoundland, Faculty of Business Administration Memorial University of Newfoundland, pp. 13-34.
- 37. Raywood, E., Douglas, H., Kapoor, K., Filipow, N., Murray, N., O’Connor, R., Stott, L., Saul, G., Kuzhagaliyev, T., Davies, G., Liakhovich, O., Van Schaik, T., Furtuna, B., Booth, J., Shannon, H., Bryon, M., Main, E. (2020), “Protocol for Project Fizzyo, an analytic longitudinal observational cohort study of physiotherapy for children and young people with cystic fibrosis, with interrupted time-series design”, BMJ Open, Vol. 10 No. 10, pp. 1-10.10.1136/bmjopen-2020-039587
- 38. Saez, Y., Baldominos, A., Isasi, P. (2016), “A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition”, Sensors, Vol. 17 No. 1, pp. 66-92.10.3390/s17010066
- 39. Sagiroglu, S., Sinanc, D. (2013), “Big data: a review”, in Proceedings of the International Conference on Collaboration Technologies and Systems (CTS ‘13), San Diego, California, USA, pp. 42-47.10.1109/CTS.2013.6567202
- 40. Schroeck, M., Schockley, R., Smart, J., Morales D. R. (2012), “Analytics: the real-world use of big data: how innovative enterprises extract value from uncertain data”, executive report, IBM Institute for Business Value and Said Business School at the University of Oxford, Somers, USA.
- 41. Singh, J., Singla, V. (2015), “Big Data: Tools and Technologies in Big Data”, International Journal of Computer Application, Vol. 112 No. 15, pp. 6-10.
- 42. Snedden, T. R., Scerpella, J., Kliethermes, S. A., Norman, R. S., Blyholder, L., Sanfilippo, J., McGuine, T. A., Heiderscheit, B. (2019), “Sport and Physical Activity Level Impacts Health-Related Quality of Life Among Collegiate Students”, American Journal of Health Promotion, Vol. 33 No. 5, pp. 675-682.10.1177/0890117118817715
- 43. Sung-Un, P., Hyunkyun, A., Dong-Kyu, D., Wi-Young, S. (2020), “Big Data Analysis of Sports and Physical Activities among Korean Adolescents”, International Journal of Environmental Research and Public Health, Vol. 17 No. 15, pp. 5577-5588.10.3390/ijerph17155577
- 44. Suthaharan, S. (2013), “Big Data Classification: Problems and challenges in network intrusion prediction with machine learning”, ACM Sigmetrics, Vol. 41 No. 4, pp. 70-73.10.1145/2627534.2627557
- 45. Wang, S., Scheider, S., Sporrel, K., Deutekom, M., Timmer, J., Krose, B. (2021), “What are good sitiations for running? A machine learning study using mobile and geographical data”, Frontiers in Public Health, Vol. 8, pp. 1-15.
- 46. Worldometer (2021), “Current world population”, available at: https://www.worldometers.info/world-population/ (6 March 2021)
- 47. Wu, X. Y., Han, L. H., Zhang, J. H., Luo, S., Hu, J. W., Sun, K. (2017), “The influence of physical activity, sedentary behavior on health-related quality of life among the general population of children and adolescents: A systematic review”, PLoS One, Vol. 12 No. 11, pp. 1-29.10.1371/journal.pone.0187668
- 48. Zhao, D., Wei, L., Wang, Z., Du., Y. (2015), “Modeling and analysis in marine big data: advandes and challenges”, Mathematical Problems in Engineering, Vol. 15 No. 2, pp. 1-15.