Abstract-The Industrial Internet promises to radically change and improve many industry's daily business activities, from simple data collection and processing to context-driven, intelligent and pro-active support of workers' everyday tasks and life. The present paper first provides insight into a typical industrial internet application architecture, then it highlights one fundamental arising contradiction: "Who owns the data is often not capable of analyzing it". This statement is explained by imaging a visionary data supply chain that would realize some of the Industrial Internet promises. To concretely implement such a system, recent standards published by The Open Group are presented, where we highlight the characteristics that make them suitable for Industrial Internet applications. Finally, we discuss comparable solutions and concludes with new business use cases.

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Computer science (CS) is a field of practical and scientific approach on computation and applications. Consequently, the CS students should be able to adjust to develop different types of software applications. However, even though video games are one type of software, they also impose additional requirements for the developers. In this paper we present the results of our qualitative studies on how prepared CS students are to function as game developers. The paper assesses the knowledge gaps between students majoring in computer science and game developer needs in two ways; a longitudinal study on a game development course and a focused case study on developing a game. Based on our results there are differences in communication and planning approaches between the CS students and game developers, and skill needs for game development content on a traditional computer science course curricula.

A cost-effective transfer of materials and tools from supplier location to construction site along with efficient information flow is defined as systematic construction logistics. Development of appropriate IT mechanisms plays an essential role for simplified production planning and elimination of wastes from broken resource. The contribution of this study in construction supply chain is to design and develop an innovative logistics management framework using context-aware and autonomous product centric system. More specifically, the proposed framework is responsive to real-world circumstances by demonstration of autonomous behaviour, and support several lean principles to improve resource and information flows. This paper addresses (i) an innovative solution for overcoming the construction logistics information flow challenges based on the intelligent product concept, (ii) a requirement analysis phase using "Quality Function Deployment" to turn the product requirements into technical specifications and (iii) implementation of a logistics management framework prototype to develop a first proof-of-concept.

Prevention of building-related illnesses and improving indoor air quality has become an emerging research area not only because of the comfort of workers in an office or the quality of the perceived air, but also because it can provide financial benefits to both employees and employers through a potential reduction in prolonged sick leaves. Therefore, building facility managers attempt to achieve the most comfortable and healthy environment conditions for the office workers. However, the parameters associated with achieved comfort vary from person to person as workers` preferences, as well physiological characteristics, are heterogeneous. In the ideal case, the indoor health parameters should be personalized based on individuals` feedback. This paper presents a computational framework for personalization of environmental parameters based on limited office workers' feedback. We propose that by using current state of the art machine learning methods it is possible to learn the preference model of individuals, by employing both the limited feedback and the relevant literature on healthrelated symptoms. The framework is explained and discussed in a potential example scenario. Evaluation based on real data is left as a future work.