Keywords: semantic app, semantic development, semantic app development, semantic mobile computing
Astract: Since the first release of the iPhone, mobile computing drew enormous software developers' attention and a huge amount of mobile apps has been built, predominantly for IOS and Android operating systems. Even though there are currently more than 1.5 million apps available for each mentioned platform, using semantic technologies on current mobile devices has not faced significant use yet. The idea of this thesis is to combine semantic technologies with the power of smart phones and to build an app which demonstrates this approach on food related scenarios. The use of semantic technologies and knowledge processing technologies embodied in a mobile app also addresses the challenges due to increasing demands for mobile applications. A semantic app is required to provide users the meaning of input data, which is in our case a photo of items in a restaurant menu containing food, which builds the communication between user and mobile app. The result of input data analysis is both graphical and textual, language-specific and contextual. The aim of this work is to prove that a semantic application is feasible on a mobile device. Therefore, the task is to implement a functional prototype of an android app that allows the user to take a photo whose textual content is translated from the original language into the language of the user. This result is returned embedded in the original photo as a display output. The core technologies for this purpose, Optical Character Recognition (OCR) and Machine Translation (MT), are already available.

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Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to system models has to take place to develop the full potential of model-driven engineering for the whole production domain. System Models are also essential to raise the level of flexibility of production systems even further in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will present ongoing work of applying and further developing model-driven techniques, such as consistency management and co-evolution support, for the production domain.

Keywords:
Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of good, bad, and ugly cross-disciplinary modeling. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.

Keywords:
Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of cross-disciplinary modeling with special emphasis on good, bad, and ugly habits. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.

Keywords:
Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of cross-disciplinary modeling with special emphasis on good, bad, and ugly habits. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.