Big data analytics in cloud gaming: Players' patterns recognition using artificial neural networks

Abstract

The Cloud Gaming model emerges with the evolution of the Cloud Computing and communication technologies. Through smartphones, PCs, tablets, consoles and other devices, people can access and use games on demand via data streaming, regardless the computing power of these devices. The Internet is the fundamental way of communication between the device and the game, which is hosted on a environment known as Cloud, enabling a large scale offer. The variety, volume, velocity, value and veracity (Big Data 5Vs) of data that is involved in these Cloud environments exceed the limits of analysis and manipulation of conventional tools, therefore, Big Data platforms are required to handle and interpret this data. The model known as Big Data Analytics is an effective and capable way to, not only work with these data, but understand its meaning, providing inputs for assertive analysis and predictive actions. A method is presented in this study to identify and analyze players' patterns in a virtual environment. With this information, it is possible to optimize user experience, revenue for developers and raise the level of control over the environment. Results are presented based on a dataset of the World of Warcraft game. By using a neural network, it was possible to identify with an average of 91% of accuracy the players' assiduity based on patterns in the game. Using the Hadoop technology and visualization tools on a Cloud based cluster, it was possible to map and identify the players' behaviors as well as their gameplay patterns.