412

The integration of engineering tools within engineering chains is an essential need of current engineering process optimization. For example, in case of production system engineering the tools for mechanical and electrical engineering and control programming shall exchange all relevant information automatically. Within this paper a methodology and an example for AutomationML based lossless data exchange among these engineering tools are presented. They are based on a domain model covering all relevant information needed to present the configuration data of an automation project.

Data exchange is a critical issue within the engineering chain of production systems. Different engineering disciplines with different engineering tools have to be networked over the complete engineering chain. Thus, lossless and consistent engineering information exchange is a critical issue. AutomationML is one candidate for an exchange data format fulfilling several requirements of the data exchange within the engineering process of production systems. But, what is about not fulfilled requirements? Will AutomationML provide capabilities to integrate additional information necessary within engineering? Within this work in progress paper first methods will be presented useable to adapt AutomationML to the needs of engineering process tool chains of different application domains.

Game analytics is a domain that focuses on the systems and methods used to analyze game-related data. In this paper we present how a visual game analytic tool can be developed to analyze player gameplay behavior. Our tool, Data Cracker, was built for monitoring gameplay in Dead Space 2, the latest game in the Dead Space franchise. We use Data Cracker as a case study to inform a larger discussion of designing a visual game analytic tool while working with a game team. Our design approach focuses on increasing the data literacy of a game team. This means getting an entire team interested and involved with game analytics. We found that building our tool during the early game development cycle, creating multiple early visual prototypes and branding the tool to the Dead Space team caused more team members to become interested in our tool. Increasing interest in analytics is also a means, we argue, for changing the common occurrence within the game industry to disband teams after a game is released. Instead, we promote the creation of "live" teams which stay attached to a game long after it is release in order to continue the analysis process. Additionally, we discuss the barriers one might face when developing game analytic tools, such as prejudice against analytics or the technical issues involved when collecting large data sets. All of these examples are presented as insights we gained while coupling analytic tool design to game development.

Soaring costs and an aging society originate the need for novel technologies in health care sector. Especially telemedicine and AAL systems are often promoted as such. Enabling patients to stay in their domestic environment while reducing costs due to hospital visits seems to be an evaluable opportunity on the one hand but introduces a lot of challenges, like interoperability and security, on the other. Interoperability is the main challenge to put emphasis on, when thinking of networks of medical devices recording patients vital data while acting in a plug-and-play manner. Thus, standards are needed that enable manufacturers to build interoperable medical devices and provide solutions for cost effective and easy to use telemedicine systems. Therefore this paper analyzes the ISO/IEEE11073 PHD standard towards its applicability in such environments, where the focus lies on self-organization and plug-and-play functionality. Furthermore we want to present research done on telemedicine/AAL systems during the SmartSenior project as well as introduce our own ISO/IEEE11073 implementation, which served as a basis for our analysis.

Video games are increasingly producing huge datasets available for analysis resulting from players engaging in interactive environments. These datasets enable investigation of individual player behavior at a massive scale, which can lead to reduced production costs and improved player retention. We present an approach for modeling player retention in Madden NFL 11, a commercial football game. Our approach encodes gameplay patterns of specific players as feature vectors and models player retention as a regression problem. By building an accurate model of player retention, we are able to identify which gameplay elements are most influential in maintaining active players. The outcome of our tool is recommendations which will be used to influence the design of future titles in the Madden NFL series.

No abstract available