EDV-gestützte Personaleinsatzplanung. Methoden und Verfahren:1. Auflage Philipp Funke
EDV-gestützte Personaleinsatzplanung. Methoden und Verfahren:1. Auflage. Philipp Funke
These proceedings consist of 19 papers, which have been peer-reviewed by international program committee and selected for the 5th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2017), which was held on June 30-July 1, 2017 in Berlin, Germany. The respective chapters discuss both theoretical and practical issues in connection with computational methods and optimization methods for knowledge engineering. The broad range of application areas discussed includes network computing, simulation, intelligent and adaptive e-learning, information retrieval, sentiment analysis, autonomous underwater vehicles, social media analysis, natural language processing, biomimetics in organizations, and cash management. In addition to pure content, the book offers many inspiring ideas and suggests new research directions, making it a valuable resource for graduate students, Ph.D. students, and researchers in Computer Science and Applied Mathematics alike.
This book presents modern techniques for the analysis of Markov chain Monte Carlo (MCMC) methods. A central focus is the study of the number of iteration of MCMC and the relation to some indices, such as the number of observation, or the number of dimension of the parameter space. The approach in this book is based on the theory of convergence of probability measures for two kinds of randomness: observation randomness and simulation randomness. This method provides in particular the optimal bounds for the random walk Metropolis algorithm and useful asymptotic information on the data augmentation algorithm. Applications are given to the Bayesian mixture model, the cumulative probit model, and to some other categorical models. This approach yields new subjects, such as the degeneracy problem and optimal rate problem of MCMC. Containing asymptotic results of MCMC under a Bayesian statistical point of view, this volume will be useful to practical and theoretical researchers and to graduate students in the field of statistical computing.
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
This new and completely updated edition is a comprehensive, easy-to-read, ´´how-to´´ guide on user research methods. You´ll learn about many distinct user research methods and also pre- and post-method considerations such as recruiting, facilitating activities or moderating, negotiating with product developments teams/customers, and getting your results incorporated into the product. For each method, you´ll understand how to prepare for and conduct the activity, as well as analyze and present the data - all in a practical and hands-on way. Each method presented provides different information about the users and their requirements (e.g., functional requirements, information architecture). The techniques can be used together to form a complete picture of the users´ needs or they can be used separately throughout the product development lifecycle to address specific product questions. These techniques have helped product teams understand the value of user experience research by providing insight into how users behave and what they need to be successful. You will find brand new case studies from leaders in industry and academia that demonstrate each method in action. This book has something to offer whether you are new to user experience or a seasoned UX professional. After reading this book, you´ll be able to choose the right user research method for your research question and conduct a user research study. Then, you will be able to apply your findings to your own products. Completely new and revised edition includes 30+% new content! Discover the foundation you need to prepare for any user research activity and ensure that the results are incorporated into your products Includes all new case studies for each method from leaders in industry and academia
This book presents a comparison between two simulation methods, namely Discrete Event Simulation (DES) and Agent Based Simulation (ABS). In our literature review we identified a gap in comparing the applicability of these methods to modelling human centric service systems. Hence, we have focused our research on reactive and different level of detail of proactive of human behaviour in service systems. The aim of the work is to establish a comparison for modelling human reactive and different level of detail of proactive behaviour in service systems using DES and ABS. To achieve this we investigate both the similarities and differences between model results performance and the similarities and differences in model difficulty performance.
Are you attracted by the promises of agile methods but put off by the fanaticism of many agile texts? Would you like to know which agile techniques work, which ones do not matter much, and which ones will harm your projects? Then you need Agile! : the first exhaustive, objective review of agile principles, techniques and tools. Agile methods are one of the most important developments in software over the past decades, but also a surprising mix of the best and the worst. Until now every project and developer had to sort out the good ideas from the bad by themselves. This book spares you the pain. It offers both a thorough descriptive presentation of agile techniques and a perceptive analysis of their benefits and limitations. Agile! serves first as a primer on agile development : one chapter each introduces agile principles, roles, managerial practices, technical practices and artifacts. A separate chapter analyzes the four major agile methods: Extreme Programming, Lean Software, Scrum and Crystal. The accompanying critical analysis explains what you should retain and discard from agile ideas. It is based on Meyer´s thorough understanding of software engineering, and his extensive personal experience of programming and project management. He highlights the limitations of agile methods as well as their truly brilliant contributions - even those to which their own authors do not do full justice. Three important chapters precede the core discussion of agile ideas: an overview, serving as a concentrate of the entire book; a dissection of the intellectual devices used by agile authors; and a review of classical software engineering techniques, such as requirements analysis and lifecycle models, which agile methods criticize. The final chapters describe the precautions that a company should take during a transition to agile development and present an overall assessment of agile ideas. This is the first book to discuss agile methods, beyond the brouhaha, in the general context of modern software engineering. It is a key resource for projects that want to combine the best of established results and agile innovations.
Dieses Buch ist ein Aufbaukurs für Fortgeschrittene, die mit den Grundlagen von Autodesk® Inventor® 2016 bereits vertraut sind. Das Programm verfügt über einen Bereich der Dynamischen Simulation, in dem komplexe Baugruppen unter Einfluss äußerer Randbedingungen, wie Kräften und Drehmomenten, berechnet und simuliert werden können. Die Ergebnisse können dann zur weiteren Bearbeitung in den Bereich der Finiten-Elemente-Methode übertragen werden. In einem komplexen Übungsbeispiel wird der Leser theoretische Grundlagen der Befehle aus dem Bereich der Dynamischen Simulation erlernen und anschließend praktisch umsetzen.
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.