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The digital transformation of our society changes the way we live, work, learn, communicate, and collaborate. The digitization of software-intensive products and services is enabled basically by four megatrends: Cloud computing, big data mobile systems, and social technologies. This disruptive change interacts with all information processes and systems that are important business enablers for the current digital transformation. The internet of things, social collaboration systems for adaptive case management, mobility systems and services for big data in cloud services environments are emerging to support intelligent user-centered and social community systems. Modern enterprises see themselves confronted with an ever growing design space to engineer business models of the future as well as their IT support, respectively. The decision analytics in this field becomes increasingly complex and decision support, particularly for the development and evolution of sustainable enterprise architectures (EA), is duly needed. With the advent of intelligent user-centered and social community systems, the challenging decision processes can be supported in more flexible and intuitive ways. Tapping into these systems and techniques, the engineers and managers of the enterprise architecture become part of a viable enterprise, i.e. a resilient and continuously evolving system that develops innovative business models.
Besides the optimisation of the car, energy-efficiency and safety can also be increased by optimising the driving behaviour. Based on this fact, a driving system is in development whose goal is to educate the driver in energy efficient and safe driving. It monitors the driver, the car and the environment and gives energy-efficiency and safety relevant recommendations. However, the driving system tries not to distract or bother the driver by giving recommendations for example during stressful driving situations or when the driver is not interested in that recommendation. Therefore, the driving system monitors the stress level of the driver as well as the reaction of the driver to a given recommendation and decideswhether to give a recommendation or not. This allows to suppress recommendations when needed and, thus, to increase the road safety and the user acceptance of
the driving system.
The evolution of Services Oriented Architectures (SOA) presents many challenges due to their complex, dynamic and heterogeneous nature. We describe how SOA design principles can facilitate SOA evolvability and examine several approaches to support SOA evolution. SOA evolution approaches can be classified based on the level of granularity they address, namely, service code level, service interaction level and model level. We also discuss emerging trends, such as microservices and knowledge-based support, which can enhance the evolution of future SOA systems.
A lot of people need help in their daily life to wash, select and manage their clothing. The goal of this work is to design an assistant system (eKlarA) to support the user by giving recommendations to choose the clothing combinations, to find the clothing and to wash the clothing. The idea behind eKlarA is to generate a system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of the stations: closets, laundry basket and washing machine in one or several places. The system uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. The first prototype of this system has been developed and tested. The test results are presented in this work.
Rapidly growing data volumes push today's analytical systems close to the feasible processing limit. Massive parallelism is one possible solution to reduce the computational time of analytical algorithms. However, data transfer becomes a significant bottleneck since it blocks system resources moving data-to-code. Technological advances allow to economically place compute units close to storage and perform data processing operations close to data, minimizing data transfers and increasing scalability. Hence the principle of Near Data Processing (NDP) and the shift towards code-to-data. In the present paper we claim that the development of NDP-system architectures becomes an inevitable task in the future. Analytical DBMS like HPE Vertica have multiple points of impact with major advantages which are presented within this paper.
In this chapter we introduce methods to improve mechanical designs by bionic methods. In most cases we assume that a general idea of the part or system is given by a set of data or parameters. Our task is to modify these free parameters so that a given goal or objective is optimized without violation of any of the existing restrictions.
To illustrate the power and the pitfalls of Bionic Optimization, we will show some examples spanning classes of applications and employing various strategies. These applications cover a broad range of engineering tasks. Nevertheless, there is no guarantee that our experiences and our examples will be sufficient to deal with all questions and issues in a comprehensive way. As general rule it might be stated, that for each class of problems, novices should begin with a learning phase. So, in this introductory phase, we use simple and quick examples, e.g., using small FE-models, linear load cases, short time intervals and simple material models. Here beginners within the Bionic Optimization community can learn which parameter combinations to use. In Sect. 3.3 we discuss strategies for optimization study acceleration. Making use of these parameters as starting points is one way to set the specific ranges, e.g., number of parents and kids, crossing, mutation radii and, numbers of generations. On the other hand, these trial runs will doubtless indicate that Bionic Optimization needs large numbers of individual designs, and considerable time and computing power. We recommend investing enough time preparing each task in order to avoid the frustration should large jobs fail after long calculation times.
Current fields of interest
(2016)
If we review the research done in the field of optimization, the following topics appear to be the focus of current development:
– Optimization under uncertainties, taking into account the inevitable scatter of parts, external effects and internal properties. Reliability and robustness both have to be taken into account when running optimizations, so the name Robust Design Optimization (RDO) came into use.
– Multi-Objective Optimization (MOO) handles situations in which different participants in the development process are developing in different directions. Typically we think of commercial and engineering aspects, but other constellations have to be looked at as well, such as comfort and performance or price and consumption.
– Process development of the entire design process, including optimization from early stages, might help avoid inefficient efforts. Here the management of virtual development has to be re-designed to fit into a coherent scheme.
...
There are many other fields where interesting progress is being made. We limit our discussion to the first three questions.
Application to CAE systems
(2016)
Due to the broad acceptance of CAD-systems based on 3D solids, the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models, where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
Due to the broad acceptance of CAD-systems based on 3D solids , the geometric data of all common CAE (Computer-Aided Engineering) software, at least in mechanical engineering, are based on these solids. We use solid models , where the space filled by material is defined in a simple and easily useable way. Solid models allow for the development of automated meshers that transform solid volumes into finite elements. Even after some unacceptable initial trials, users are able to generate meshes of non-trivial geometries within minutes to hours, instead of days or weeks. Once meshing had no longer been the cost limiting factor of finite element studies, numerical simulation became a tool for smaller industries as well.
In the early days of automated meshing development, there were discussions over the use of tetragonal (Fig. 4.1) or hexagonal based meshes. But, after a short period of time, it became evident, that there were and will always be many problems using automated meshers to generate hexagonal elements . So today nearly all automated 3D-meshing systems use tetragonal elements .
Motivation
(2016)
Since human beings started to work consciously with their environment, they have tried to improve the world they were living in. Early use of tools, increasing quality of these tools, use of new materials, fabrication of clay pots, and heat treatment of metals: all these were early steps of optimization. But even on lower levels of life than human beings or human society, we find optimization processes. The organization of a herd of buffalos to face their enemies, the coordinated strategies of these enemies to isolate some of the herd’s members, and the organization of bird swarms on their long flights to their winter quarters: all these social interactions are optimized strategies of long learning processes, most of them the result of a kind of collective intelligence acquired during long selection periods.
The question of why individuals adopt information technology has been present in the information systems research since the past quarter century. One of the most used models for predicting the technology usage was introduced by Fred David: The Technology Acceptance Model (TAM). It describes the influence of perceived usefulness and perceived ease of use on attitude, behavioral intention and system usage. The first two mentioned factors in turn are influenced by external variables. Although a plethora of papers exists about the TAM , an extensive analysis of the role of the external variables in the model is still missing. This paper aims to give an overview ove the most important variables. In an extensive literature review, we identified 763 relevant papers, found 552 unique single extenal variables, characterized the most important of them, and described the frequency of their appearance. Additionally, we grouped these variables into four categories (organizational characteristis, system characteristics, user personal characteristics, and other variables). Afterwards we discuss the results and show implications for theory and practice.
It is known that the costs related with drug research and development (R&D) and the timelines to develop a new drug increased over the past years. In parallel, the success rates of drug projects along the pharmaceutical R&D phases are still very low, and the outcome of all R&D efforts is stagnating. In consequence, the R&D efficiency defined as the financial investment per drug has been steadily decreasing. As innovation is the major growth driver of the pharmaceutical industry, reliable data on R&D efficiency and new concepts to overcome these challenges are of great interest for R&D managers and the sustainability of the pharmaceutical industry as a whole. This book chapter reviews publications on R&D performance indicators of the past years, such as the success rates and timelines per phase. Additionally, it illustrates the factors influencing the success rates, timelines, and costs of pharmaceutical R&D most and, thus, the denominators of the R&D efficiency.
The efficiency of pharmaceutical research and development (R&D) reflected by increasing costs of R&D, long timelines, and low probabilities of technical and regulatory success decreased continuously in the past years. Today, the costs for discovering and developing a new drug are enormously high with more than USD 2 billion per new molecular entity (NME), while the average overall success of a research project to provide an NME is in the single-digit percentage rate, and the total timelines of R&D easily exceeds 10 years questioning the return on investment (ROI) of pharmaceutical R&D. As a consequence and also caused by numerous patent expirations of blockbuster drugs that increased the pressure to return to an acceptable ROI, the pharmaceutical industry addressed this challenge and the related causes and identified several actions that need to be taken to increase the output/input ratio of R&D. This book chapter will review the pipeline sizes and the R&D investments of multinational pharmaceutical companies, will describe new processes that have been implemented to increase the reach and to reduce costs of pharmaceutical R&D, and it will illustrate new innovation models that were developed to increase the R&D efficiency.
The reduced research and development (R&D) efficiency, strong competition from generics, increased cost pressure from payers, and an increased biological complexity of new target indications have resulted in a rethinking and a change from a traditional and more closed R&D model in the pharmaceutical industry toward the new paradigm of open innovation. In the past years, pharmaceutical companies have broadened their external networks toward research collaborations with academic institutes, technology providers, or codevelopment partners. To fulfill the demand to reduce timelines and costs, research-based pharmaceutical companies started to outsource R&D activities. In addition, internal R&D processes were adjusted to the more open R&D model and new processes such as alliance management were established. The corporate frontier of pharmaceutical companies became permeable and more open. As a result, the focus of pharmaceutical R&D expanded from a purely internal toward a mixed internal and external model. Today, the U.S. pharmaceutical company Eli Lilly may have established the most open model toward external innovation, as it has integrated its innovation processes with its business model. Other companies are following this more open R&D model with newer concepts such as new frontier sciences, drug discovery alliances, private public partnerships, innovation incubators, virtual R&D, crowdsourcing, open source innovation, and innovation camps.
Digital companies need information systems to implement their business processes end-to-end. BPM systems are promising candidates for that, because they are highly adaptable due to their business process model-driven operation mode. End-to-end processes contain different types of sub-processes that are either procedural, data-driven or business rule-based. Modern BPM systems support modeling notations for all these types of sub-processes. Moreover, end-to-end processes contain parts of shadow processing, so consequently, they must be supported in a performant way, too. BPMN seems to be the adequate notation for modeling these parts due to its procedural nature. Further, BPMN provides several elements that enable the modeling of parallel executions which are very interesting for accelerating shadow processing parts of the process. The present paper will observe the limitations and potentials of BPM systems for a high-performance execution of BPMN models representing shadow processing parts of a business process.
Stress is recognized as a predominant disease with raising costs for rehabilitation and treatment. Currently there are several different approaches that can be used for determining and calculating the stress levels. Usually the methods for determining stress are divided in two categories. The first category do not require any special equipment for measuring the stress. This category useless the variation in the behaviour patterns that occur while stress. The core disadvantage for the category is their limitation to specific use case. The second category uses laboratories instruments and biological sensors. This category allow to measure stress precisely and proficiently but on the same time they are not mobile and transportable and do not support real-time feedback. This work presents a mobile system that provides the calculation of stress. For achieving this, the of a mobile ECG sensor is analysed, processed and visualised over a mobile system like a smartphone. This work also explains the used stress measurement algorithm. The result of this work is a portable system that can be used with a mobile system like a smartphone as visual interface for reporting the current stress level.
Industrie 4.0 - Ausblick
(2016)
Für Unternehmen ist es wichtig, frühzeitig die strategischen Weichen für ihre Industrie 4.0-Stoßrichtung zu stellen und Erfahrung im Umgang mit Industrie 4.0-Technologien aufzubauen. Allerdings werden einige der Industrie 4.0-relevanten Technologien voraussichtlich erst in 5 bis 10 Jahren ihr Effizienzpotential voll ausschöpfen können. Die Einführung von Industrie 4.0 betrifft nahezu alle Bereiche eines Unternehmens und ist deshalb nicht nur als digitale Transformation, sondern auch als Kulturwandel in der Organisation zu verstehen, zu planen und aktiv zu managen. Themen wie Datenschutz und IT-Sicherheit sind nicht nur wichtige Voraussetzungen für eine erfolgreiche Industrie 4.0-Einführung, sondern müssen als wesentliche Akzeptanz- und Erfolgsfaktoren konsequent und durchgängig in den digitalen Systemen verankert werden.
KMUs sehen sich häufig aus finanziellen Gründen nicht in der Lage, in grundlegende Technologien der Industrie 4.0 zu investieren. So wird als Hauptvorbehalt eine vermeintlich schlechte Kosten-Nutzen-Relation bzw. langfristige Pay-Back-Zyklen angegeben. Die aktuellen Herausforderungen liegen derzeit eher bei der immer weiter voranschreitenden Internationalisierung sowie dem ansteigenden Innovationsdruck durch den Wettbewerb. Natürlich ist bekannt, dass die zunehmende Vernetzung der Produktionsanlagen in der Industrie 4.0 zudem Risiken in der IT- und Datensicherheit mit sich bringt. Auch Datenqualitäts-, Stabilitäts-, Schnittstellenprobleme oder rechtliche Probleme sind ausschlaggebend für die Verunsicherung der Unternehmen. Durch die zukünftig immer weiter ansteigende Vernetzung zwischen Unternehmen und Stakeholdern, müssen sich insbesondere Zulieferunternehmen in der Pflicht sehen, das Thema Industrie 4.0 aufzugreifen und sich damit auseinander zu setzen. Gerade diese Unternehmen müssen sich vor Augen führen, dass sie nur durch den zukünftigen Einsatz geeigneter Informations- und Kommunikationstechnologien noch in der Lage sein werden, Teil der Wertschöpfungskette zwischen ihren Kunden und Lieferanten zu sein.
Unternehmen befassen sich in jüngster Zeit verstärkt mit der Nutzung von Social Media in der internen Kommunikation und Zusammenarbeit. So genannte Enterprise Social Networks (ESN) bieten integrierte Plattformen mit Profilen, Blogs, gemeinsamer Dokumentenverwaltung, Wikis, Chats, Gruppen- und Kommentarfunktionen für die unternehmensinterne Anwendung. Sehr häufig sind damit umfangreiche Investitionen verbunden. Die Budgets werden im Kern für die IT verwendet – „weiche Faktoren“ bleiben häufig außen vor. Dies kann zu erheblichen Problemen bei der Akzeptanz entsprechender Plattformen führen. Daher sind weitere Maßnahmen im Bereich der Steuerung der Einführung und des Betriebs von ESN erforderlich, die sich unter dem Begriff der Governance zusammenfassen lassen. Das Konstrukt Governance bezieht sich auf Art und Umfang der Rollen und Aufgaben zur Steuerung der Nutzung von ESN. Der vorliegende Beitrag beleuchtet mögliche Governancemodelle für die Einführung und Weiterentwicklung von ESN. Die Resultate der vorliegenden Forschung wurden auf der Grundlage einer fundierten Literaturanalyse sowie der explorativen Befragung verantwortlicher Executives für die Nutzung von ESN in deutschen Großunternehmen erzielt. Dabei weisen die Implikationen der qualitativen Datenanalyse auf Zusammenhänge hin, die sich als Ausgangshypothesen für weitere Forschungsarbeiten nutzen lassen.
Enterprise Social Networks : Einführung in die Thematik und Ableitung relevanter Forschungsfelder
(2016)
Die Relevanz von Enterprise Social Networks (ESN) für den Arbeitsalltag in Wissensorganisationen steigt. Diese Netzwerke unterstützen die Kommunikation, Zusammenarbeit und das Wissensmanagement in Unternehmen. Der vorliegende Beitrag beinhaltet eine Einführung in das Themengebiet ESN und skizziert Einsatzmöglichkeiten, Potenziale und Herausforderungen. Er gibt einen Überblick zu wesentlichen Fachartikeln, die eine Übersicht zu Forschungsarbeiten im Bereich ESN beinhalten. Anschließend werden einzelne Forschungsbeiträge analysiert und weitere Forschungspotenziale abgeleitet. Dies führt zu acht Erfolg versprechenden Bereichen für die weitere Forschung: 1) Nutzerverhalten, 2) Effekte des Einsatzes von ESN, 3) Management, Leadership und Governance für ESN, 4) Wertbestimmung und Erfolgsmessung, 5) kulturelle Auswirkungen, 6) Architektur und Design von ESN, 7) Theorien, Forschungsdesigns und Methoden, sowie 8) weitere Herausforderungen in Bezug auf ESN. Der Beitrag charakterisiert diese Bereiche und formuliert exemplarisch offene Fragestellungen für die zukünftige Forschung.