The video game industry is becoming increasingly important due to its revenues and growing capabilities. User eXperience (UX) is an important factor which contributes to the acceptance of a video game. The UX is usually assessed at the end of the development process, and for this reason it is difficult to ensure an adequate level of interactive experience between computer game and players. Cancelation of projects or even bankruptcy of a company can be caused by bad management of UX. In this paper, we propose the game experience management (GEM), a method to evaluate, manage, measure and track the UX from early stages of computer game development. In order to compare the proposal against a method comprised by conventional approaches, teams of master degree students were formed for developing six tower defense games for teaching basic multiplication operations; surveys were conducted to compare the UX of games. In this setting, we find that games developed with GEM significantly improve UX by increasing the puppetry and consequently reducing player frustration.
The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.
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