01639nas a2200253   4500000000100000000000100001008004100002260001200043653003700055653002000092653001300112653002500125653002500150100001400175700003200189700002300221700002000244245011300264856009800377300001000475490000600485520088000491022001401371       2016                            d  c03/201610aBehaviour Control and Monitoring10aUser Experience10aBig Data10aHidden Markov Models10aReal-Time Prediction1 aYago Saez1 aAlejandro Baldominos Gómez1 aEsperanza Albacete1 aIgnacio Marrero00aReal-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/02/ijimai20163_6_7_pdf_23096.pdf  a44-510 v33 aThis paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies.  a1989-1660