Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.

Acknowledging the user context, e.g., position and activity, provides a natural way to adapt applications according to the user needs. How to actually capture and exploit context, however, is not self-evident and it is tempting to assign the related responsibilities to individual context-consuming applications. Unfortunately, this confuses the user, complicates application development and hinders context-aware semantic computing as a research discipline. In this article, we outline context-aware semantic computing research topics and the state-of-the-art mobile application development frameworks of special interest to us, acknowledging best practices for accessing and modeling sensor context. From the integrated point of view, context-aware semantic computing is demonstrated in terms of a software component called context engine. In order to better understand how theory is tied with practice, we also introduce a simple context engine prototype. Finally, we use the research background and the empirical setting to discuss the significant problems and relevant research directions in context-aware semantic processing.

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Purpose: The purpose of this study is to characterize, analyze, and demonstrate machine-understandable semantic process for validating, integrating, and processing technical design information. This establishes both a vision and tools for information reuse and semi-automatic processing in engineering design projects, including virtual machine laboratory applications with generated components.

Design/methodology/approach: The process model has been developed iteratively in terms of action research, constrained by the existing technical design practices and assumptions (design documents, expert feedback), available technologies (pre-studies and experiments with scripting and pipeline tools), benchmarking with other process models and methods (notably the RUP and DITA), and formal requirements (computability and the critical information paths for the generated applications). In practice, the work includes both quantitative and qualitative components.

Findings: Technical design processes may be greatly enhanced in terms of semantic process thinking, by enriching design information, and automating information validation and transformation tasks. Contemporary design information, however, is mainly intended for human consumption, and needs to be explicitly enriched with the currently missing data and interfaces. In practice, this may require acknowledging the role of technical information or knowledge engineer, to lead the development of the semantic design information process in a design organization. There is also a trade-off between machine-readability and system complexity that needs to be studied further, both empirically and in theory.

Research limitations/implications: The conceptualization of the semantic process is essentially an abstraction based on the idea of progressive design. While this effectively allows implementing semantic processes with, e.g., pipeline technologies, the abstraction is valid only when technical design is organized into reasonably distinct tasks.

Practical implications: Our work points out a best practice for technical information management in progressive design that can be applied on different levels.

Social implications: Current design processes may be somewhat impaired by legacy practices that do not promote information reuse and collaboration beyond conventional task domains. Our work provides a reference model to analyze and develop design activities as formalized work-flows. This work should lead into improved industry design process models and novel CAD/CAM/PDM applications, thereby strengthening industry design processes.

Originality/value: While extensively studied, semantic modeling in technical design has been largely dominated by the idea of capturing design artifacts without a clear rationale why this is done and what level of detail should be favored in models. In the semantic process presented in this article, the ...

Abstract.Creation of virtual machine laboratories -simulated planning and learning environments demonstrating function and structure of working machines -often involve a lot of manual labor. A notable source of the labor is the programming required due to changes in structural and functional models of a system. As a result, rapid prototyping of a virtual machine laboratory becomes difficult, if not impossible. We argue that by using a combination of semantic modeling and prototyping with a web-based system, more rapid development of virtual machine laboratories can be achieved. In this paper, we present the design and implementation of a semantic, web-based virtual machine laboratory prototyping environment. Application of the environment to a case example is also described and discussed.

Notwithstanding the significant advances in contextaware computing in pervasive computing and self-adaptive systems, there is still much more to be desired in providing better context services. The number of sensors deployed world-wide increases very rapidly. The Internet of Things, amongst others, generates vast amounts of data of many different data types. How data are used is essential to improve user experience and efficiencies of the systems in which they occur. We explain how familiar concepts of Process Mining strengthen generalised sensor context services. We present a laboratory case to explain the approach. By way of a real-world example, we confirm the viability of using Process Mining to strengthen context-aware computing.