Skript aus dem Jahr 2003 im Fachbereich Informatik - Theoretische Informatik, Duale Hochschule Baden Württemberg Mosbach, Veranstaltung: Operations Research, Sprache: Deutsch, Abstract: Während die vordergründigen Handwerkszeuge des Informatikers, Software und Hardware, einem kaum greifbaren Wandel unterliegen - was heute gelernt wird, ist morgen schon wieder veraltet - stehen die zugrunde legenden Strukturen als unverrückbare Invarianten fest. Ihr Verständnis stellt somit eine notwendige Bedingung sowohl für tiefer gehende Einsichten, als auch für einen verstandesgemäßen Gebrauch der Anwendungen dar. In der Informatik sind diese Strukturen insbesondere die Logik und daran anknüpfend der Algorithmus. Beide haben eine mehr als zweitausendjährige Geschichte (vgl. den berühmten Euklid´schen Divisionalalgorithmus!). Während diese Begriffe allgemein im Rahmen der Theoretischen Informatik abgehandelt werden, sollen hier nun darauf aufbauend, exemplarisch konkrete Algorithmen und insbesondere die fundamentalen Entwurfstechniken dargestellt werden. Diese wurden im Wesentlichen in den sechziger Jahren des vorigen Jahrhunderts entwickelt und gelten bis heute unverändert. Entsprechend dem Studiengang Wirtschaftsinformatik, für den diese Vorlesung konzipiert ist, werden beispielhaft einige ökonomische Anwendungen aufgezeigt. Die Monographie stellt die Grundlage einer dreißigstündigen Vorlesung an der Dualen Hochschule Mosbach dar. Sie schließt an die Vorlesung über theoretische Informatik an und setzt Grundlagen in diesem Bereich im Wesentlichen voraus.
Provides a strategic framework for people who practice UX research who wish to be heard by their stakeholders. This title gives you the techniques needed to involve stakeholders throughout the process of planning, execution, analysis, and reporting UX research.
Bachelor Thesis from the year 2009 in the subject Computer Science - Commercial Information Technology, grade: 1,3, University of Frankfurt (Main) (Institute of Information Systems), language: English, abstract: The Information Systems (IS) research discipline is undergoing a serious identity crisis, seeking its sphere of activity to be relevant in practice and rigorous in scientific considerations. One reason for this is the strengthening of the Design Science approach. This new discipline developed as a synergy from aspects of engineering, architecture, and industrial design and is employed in the design of IT artifacts and software systems. Design Science is becoming a powerful trend in IS research (Vahidov 2006). It gives the IS discipline a new and more detailed focal point as pertains to the application of software and IT artifact development which is growing in importance in IS research over the time (Weber 2003; Orlikowski and Iacono 2001; Cross 2001). IS practitioners ask for new and innovative design approaches, dealing with the evolving organizational and inter-organizational tasks. The way these tasks are executed, in close cooperation with the practical business world, seems to be insufficiently considered. The debate in IS research is carried out between traditional scientists and the knowledge-producing researchers/practitioners and ´´it could be argued that research aimed at developing IT systems, at improving IT practice, has been more successful and important than traditional scientific attempts to understand it´´ (March and Smith 1995, p. 252). IS researchers are mainly focused on the behavioral impact of new IT solutions within a business unit. These concepts are needed to describe the relationship between the humans and the technology. However, this way of conducting research is descriptive and evaluative. Instead of telling ´´what is´´ or ´´what will be´´, Design Science is giving guidance as to ´´how to do´´ things (Walls et al. 1992). The importance of this new approach is given through the rapid development of business needs and the increased necessity to solve business problems through the implementation of IT solutions. The knowledge base for designing new solutions has not yet been fully developed. IT consultants borrow knowledge from reference disciplines and apply this knowledge to present problems. This way of providing solutions is not compatible with Design Science as an area of research. A relevant design approach needs to give new answers to phenomena thus far unsolved. However, the IS discipline has not yet established a solid groundwork for Design Science within its discipline.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.
Covering research topics from system software such as programming languages, compilers, runtime systems, operating systems, communication middleware, and large-scale file systems, as well as application development support software and big-data processing software, this book presents cutting-edge software technologies for extreme scale computing. The findings presented here will provide researchers in these fields with important insights for the further development of exascale computing technologies. This book grew out of the post-peta CREST research project funded by the Japan Science and Technology Agency, the goal of which was to establish software technologies for exploring extreme performance computing beyond petascale computing. The respective were contributed by 14 research teams involved in the project. In addition to advanced technologies for large-scale numerical computation, the project addressed the technologies required for big data and graph processing, the complexity of memory hierarchy, and the power problem. Mapping the direction of future high-performance computing was also a central priority.
This book combines multiple research methods, experiment, survey, and design science, as well as traditional measurements and neurophysiological techniques that can capture a variety of cognitive behaviors in human information processing, providing more solid and comprehended research findings. While the focus of the book is the modelling of process models and rules, the methods and techniques used in this book can also be adopted and applied to broader conceptual modelling research incorporating a variety of notations (e.g. UML, ER diagrams) or ontologies. It is a revised version of the PhD dissertation written by the author at the School of Information Technology and Electrical Engineering of the University of Queensland, Australia. In 2018, the PhD dissertation won the ´´CAiSE PhD Award,´´ granted to outstanding PhD theses in the field of information systems engineering.
The twenty last years have been marked by an increase in available data and computing power. In parallel to this trend, the focus of neural network research and the practice of training neural networks has undergone a number of important changes, for example, use of deep learning machines. The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world´s most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems.