Data alone are worth almost nothing. While data collection is increasing exponentially worldwide, a clear distinction between retrieving data and obtaining knowledge has to be made. Data are retrieved while measuring phenomena or gathering facts. Knowledge refers to data patterns and trends that are useful for decision making. Data interpretation creates a challenge that is particularly present in system identification, where thousands of models may explain a given set of measurements. Manually interpreting such data is not reliable. One solution is to use data mining. This book thus proposes an integration of techniques from data mining, a field of research where the aim is to find knowledge from data, into an existing multiple-model system identification methodology. In addition to providing information about the candidate model space, data mining is found to be a valuable tool for supporting decisions related to subsequent sensor placement.
Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen ´´Analytics´´-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.
This book is a comprehensive introduction to the methods and algorithms of modern data analytics. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. This book has been used for more than ten years in the Data Mining course at the Technical University of Munich. Much of the content is based on the results of industrial research and development projects at Siemens.
Big Data ist ein aktuelles Trendthema, doch was versteckt sich dahinter? Big Data beschreibt Daten, die gross oder schnelllebig sind. Big Data bedeutet aber auch, sich mit vielfältigen Datenquellen und Datenformaten zu beschäftigen. Diese Lektüre soll daher eine Einführung in das Ökosystem Big Data sein. Anhand einfacher Beispiele werden Methoden und Technologien zur Handhabung von Big Data aufgezeigt.
´´Data Modeling Essentials, Third Edition´´ provides expert tutelage for data modelers, business analysts and systems designers at all levels. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. The third edition of this popular book retains its distinctive hallmarks of readability and usefulness, but has been given significantly expanded coverage and reorganized for greater reader comprehension. Authored by two leaders in the field, ´´Data Modeling Essentials, Third Edition´´ is the ideal reference for professionals and students looking for a real-world perspective. Thorough coverage of the fundamentals and relevant theory. Recognition and support for the creative side of the process. Expanded coverage of applied data modeling includes new chapters on logical and physical database design. New material describing a powerful technique for model verification. Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict. Extensive online component including course notes and other teaching aids (www.mkp.com). UML diagrams now available! Visit the companion site for more details. Click here to view a book review by Steve Hoberman!
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.
Datenmodelle für Kernfunktionen, die in nahezu allen Geschäftsbereichen eine Rolle spielen: Dieses zweibändige, überarbeitete Handbuch in der 2. Auflage zeigt Datenbankprogrammierern, wie sie zwei Drittel der üblichen Entwicklungszeit sparen können! Neu in diesem 1. Band ist ein Kapitel zu Data Marts für die Finanzanalyse; über 30% des Stoffes wurde außerdem sorgfältig aktualisiert. Aufgenommen wurden hochaktuelle Datenmodelle, mit denen die Autoren seit dem Erscheinen der Erstauflage Erfahrungen sammeln konnten. Eine separat erhältliche CD-ROM beinhaltet sämtliche Datenmodelle in einer Form, die leicht in gebräuchliche kommerzielle Datenbanken zu integrieren ist.
This textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. Features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those from a different language background.
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods´ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information. The basic principle of metaheuristics is to sample a set of solutions which is large enough to be completely sampled. As metaheuristics make few assumptions about the optimization problem to be solved, they may be put to use in a variety of problems. Metaheuristics do not however, guarantee that a globally optimal solution can be found on some class of problems since most of them implement some form of stochastic optimization. Hence the solution found is often dependent on the set of random variables generated. By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics. As such, they are useful approaches for optimization problems. Even though the metaheuristics are robust enough to yield optimum solutions, yet they often suffer from time complexity and degenerate solutions. In an effort to alleviate these problems, scientists and researchers have come up with the hybridization of the different metaheuristic approaches by conjoining with other soft computing tools and techniques to yield failsafe solutions. In a recent advancement, quantum mechanical principles are being employed to cut down the time complexity of the metaheuristic approaches to a great extent. Thus, the hybrid metaheuristic approaches have come a long way in dealing with the real life optimization problems quite successfully. Proper and faithful analysis of digital images has been in the helm of affairs in the computer vision research community given the varied amount of uncertainty inherent in digital images. Images exhibit varied uncertainty and ambiguity of information and hence understanding an image scene is far from being a general procedure. The situation becomes even graver when the images become corrupt with noise artifacts. The applications of proper analysis of images encompass a wide range of applications which include image processing, image mining, image inpainting, video surveillance, intelligent transportation systems to name a few. One of the notable areas of research in image analysis is the estimation of age progression in human beings through analysis of wrinkles in face images, which can be further utilized for tracing unknown or missing persons. Hurdle detection is one of the common tasks in robotic vision that have been done through image processing, by identifying different type of objects in the image and then calculating the distance between robot and hurdles. Image analysis has a lot to contribute in this direction. Processing of color images takes the problem of image analysis to a new dimension. Apart from processing and analysis of the color gamut which involves a lot of computational overhead, the problem also involves analysis of the varied amount of uncertainty exhibited by the color images. A video is a very fast movement of pictures. Video analysis as a part of image analysis focuses on Shot Boundary Detection (SBD), dissolve detection, detection of gradual transitions and detection of fade ins/outs. Recent trends in research on image analysis rely heavily on pose and gesture analysis. Typical applications include human-machine interaction, behavior analysis, video surveillance, annotation, search and retrieval, motion capture for the entertainment industry and interactive web-based applications. Real-time video analysis algorithms mainly focus on hand and head tracking and gesture analysis. A faithful gesture recognition algorithm can be implemented with techniques borrowed from computer vision and image processing. The evolution of the functional Magnetic Resonance Imaging (fMRI) has led to proper analysis of the study mechanisms in the brain. Several statistic