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Affiliate systems are a crucial piece of today’s online advertising. In affiliate systems, web traffic is directed from certain sites displaying ads to the websites of those company whose products or services are advertised. The way in which these ads are monetized is diverse and can respond to different models. In many cases, affiliates establish a cost based on impressions (displays of the ad) or on clicks. However, more intricate models are becoming widespread, such as the cost per action, where the affiliate incomes are due to the users performing certain actions in the target website. In particular, in the world of iGaming, it is frequent that affiliates charges are based on registrations, deposits or money lost on bets. In this scenario, Blockverse is a tool whose objective is to record transactions occurring in affiliate systems at large scale, using a permissioned blockchain implemented atop state-of-the-art cloud technology. Additionally, the system will be able to execute smart deals that generate income for affiliates based on the agreed conditions, and to provide real-time analytics in the context of the affiliate system.

This paper presents DataCare, a solution for intelligent healthcare management. This product is able not only to retrieve and aggregate data from different key performance indicators in healthcare centers, but also to estimate future values for these key performance indicators and, as a result, fire early alerts when undesirable values are about to occur or provide recommendations to improve the quality of service. DataCare’s core processes are built over a free and open-source cross-platform document-oriented database (MongoDB), and Apache Spark, an open-source cluster-computing framework. This architecture ensures high scalability capable of processing very high data volumes coming at fast speed from a large set of sources. This article describes the architecture designed for this project and the results obtained after conducting a pilot in a healthcare center. Useful conclusions have been drawn regarding how key performance indicators change based on different situations, and how they affect patients’ satisfaction.

This 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.

This Special Issue focuses in cases that explore the relationship between Artificial Intelligence and marketing, as well as neuroscience. AI can be combined with specific neuroscience techniques to achieve a more successful and profitable neuromarketing. For this Special Issue, we have found that descriptions of successful use cases are highly valuable to help researchers identify fields where novel applications of AI can enhance the outcome of digital marketing and neuroscience.

This paper proposes the design of an evolutionary algorithm for building classifiers specifically aimed towards performing classification and sentiment analysis over texts. Moreover, it has properties taken from Artificial Immune Systems, as it tries to resemble biological systems since they are able to discriminate harmful from innocuous bodies (in this case, the analogy could be established with negative and positive texts respectively). A framework, namely OpinAIS, is developed around the evolutionary algorithm, which makes it possible to distribute it as an open-source tool, which enables the scientific community both to extend it and improve it. The framework is evaluated with two different public datasets, the first involving voting records for the US Congress and the second consisting in a Twitter corpus with tweets about different technology brands, which can be polarized either towards positive or negative feelings; comparing the results with alternative machine learning techniques and concluding with encouraging results. Additionally, as the framework is publicly available for download, researchers can replicate the experiments from this paper or propose new ones.