Knowledge Discovery in Databases (KDD) ist ein aktuelles Forschungs- und Anwendungsgebiet der Informatik. Ziel des KDD ist es, selbständig entscheidungsrelevante, aber bisher unbekannte Zusammenhänge und Verknüpfungen in den Daten großer Datenmengen zu entdecken und dem Analysten oder dem Anwender in übersichtlicher Form zu präsentieren. Die Autoren stellen die Techniken und Anwendungen dieses interdisziplinären Gebiets anschaulich dar.
A recent survey stated that 52% of embedded projects are late by 4-5 months. This book can help get those projects in on-time with design patterns. The author carefully takes into account the special concerns found in designing and developing embedded applications specifically concurrency, communication, speed, and memory usage. Patterns are given in UML (Unified Modeling Language) with examples including ANSI C for direct and practical application to C code. A basic C knowledge is a prerequisite for the book while UML notation and terminology is included. General C programming books do not include discussion of the contraints found within embedded system design. The practical examples give the reader an understanding of the use of UML and OO (Object Oriented) designs in a resource-limited environment. Also included are two chapters on state machines. The beauty of this book is that it can help you today. . Design Patterns within these pages are immediately applicable to your project Addresses embedded system design concerns such as concurrency, communication, and memory usage Examples contain ANSI C for ease of use with C programming code
This book presents a contemporary view of the role of information quality in information fusion and decision making, and provides a formal foundation and the implementation strategies required for dealing with insufficient information quality in building fusion systems for decision making. Information fusion is the process of gathering, processing, and combining large amounts of information from multiple and diverse sources, including physical sensors to human intelligence reports and social media. That data and information may be unreliable, of low fidelity, insufficient resolution, contradictory, fake and/or redundant. Sources may provide unverified reports obtained from other sources resulting in correlations and biases. The success of the fusion processing depends on how well knowledge produced by the processing chain represents reality, which in turn depends on how adequate data are, how good and adequate are the models used, and how accurate, appropriate or applicable prior and contextual knowledge is. By offering contributions by leading experts, this book provides an unparalleled understanding of the problem of information quality in information fusion and decision-making for researchers and professionals in the field.
This book constitutes the proceedings of the Third International Conference on Codes, Cryptology and Information Security, C2SI 2019, held in Rabat, Morocco, in April 2019. The 19 regular papers presented together with 5 invited talks were carefully reviewed and selected from 90 submissions. The first aim of this conference is to pay homage to Said El Hajji for his valuable contribution in research, teaching and disseminating knowledge in numerical analysis, modeling and information security in Morocco, Africa, and worldwide. The second aim of the conference is to provide an international forum for researchers from academia and practitioners from industry from all over the world for discussion of all forms of cryptology, coding theory, and information security.
This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.
The next generation of problems will not have deterministic solutions - the solutions will be statistical that rely on mountains, or mounds, of data. Bayesian methods offer a very flexible and extendible framework to solve these types of problems. For programming students with minimal background in mathematics, this example-heavy guide emphasizes the new technologies that have allowed the inference to be abstracted from complicated underlying mathematics. Using Bayesian Methods for Hackers, students can start leveraging powerful Bayesian tools right now -- gradually deepening their theoretical knowledge while already achieving powerful results in areas ranging from marketing to finance.
This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues. Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them. Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank. Features: · An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering · Restructured to make the logics more straightforward and sections self-contained Core Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.
Understanding how to turn numbers into usable insights is a significant challenge for those who work with data on a daily basis. Thinking with Data provides a concise framework and key insights to help data people uncover the real problem to be solved as well as how to approach, organize, and analyze potential results. By drawing from rhetoric studies, design thinking, and his data strategy consultancy experience, author Max Shron shows you how focusing on the why will help you create usable insights from your company´s data jumble. Many analysts are too concerned with tools and techniques for cleansing, modeling, and visualizing datasets and not concerned enough with asking the right questions. In this practical guide, data strategy consultant Max Shron shows you how to put the why before the how, through an often-overlooked set of analytical skills. Thinking with Data helps you learn techniques for turning data into knowledge you can use. You?ll learn a framework for defining your project, including the data you want to collect, and how you intend to approach, organize, and analyze the results. You?ll also learn patterns of reasoning that will help you unveil the real problem that needs to be solved. * Learn a framework for scoping data projects * Understand how to pin down the details of an idea, receive feedback, and begin prototyping * Use the tools of arguments to ask good questions, build projects in stages, and communicate results * Explore data-specific patterns of reasoning and learn how to build more useful arguments * Delve into causal reasoning and learn how it permeates data work * Put everything together, using extended examples to see the method of full problem thinking in action
SysML extends UML with powerful systems engineering capabilities for modeling a far wider spectrum of systems, and effectively capturing all aspects of a system?s design. Now, there?s a go-to reference for everyone who wants to start creating accurate and useful system models with SysML. Drawing on his pioneering experience creating models for Lockheed Martin and NASA, Lenny Delligatti illuminates SysML?s core components, and shows how to use them even under tight deadlines and other constraints. You needn?t know all of SysML to create effective models: SysML Distilled quickly teaches what you do need to know, and helps you deepen your knowledge incrementally as the need arises. Coverage includes: How SysML extends and improves UML, and how to immediately put it to practical use How to use SysML as a foundation for Model-Based System Engineering (MBSE) or Model-Based Engineering (MBE) What to know before you start an SysML modeling project How to use key SysML diagrams for block definitions, internal blocks, use cases, activities, sequences, state machines, parametrics, requirements, and packages How to use allocations to define cross-cutting relationships And much more ? including appendices presenting complete SysML notation, identifying changes between SysML versions, and identifying authoritative sources for more information Product Description The Systems Modeling Language (SysML) extends UML with powerful systems engineering capabilities for modeling a wider spectrum of systems and capturing all aspects of a system?s design. SysML Distilled is the first clear, concise guide for everyone who wants to start creating effective SysML models. (Drawing on his pioneering experience at Lockheed Martin and NASA, Lenny Delligatti illuminates SysML?s core components and provides practical advice to help you create good models and good designs. Delligatti begins with an easy-to-understand overview of Model-Based Systems Engineering (MBSE) and an explanation of how SysML enables effective system specification, analysis, design, optimization, verification, and validation. Next, he shows how to use all nine types of SysML diagrams, even if you have no previous experience with modeling languages. A case study running through the text demonstrates the use of SysML in modeling a complex, real-world sociotechnical system. Modeled after Martin Fowler?s classic UML Distilled, Delligatti?s indispensable guide quickly teaches you what you need to know to get started and helps you deepen your knowledge incrementally as the need arises. Like SysML itself, the book is method independent and is designed to support whatever processes, procedures, and tools you already use. Coverage Includes Why SysML was created and the business case for using it Quickly putting SysML to practical use What to know before you start a SysML modeling project Essential concepts that apply to all SysML diagrams SysML diagram elements and relationships Diagramming block definitions, internal structures, use cases, activities, interactions, state machines, constraints, requirements, and packages Using allocations to define mappings among elements across a model SysML notation tables, version changes, and sources for more information Features + Benefits The most practical introduction to SysML Covers all essential diagrams: block definitions, internal blocks, use cases, activities, sequences, state machines, parametrics, requirements, and packages How to use SysML to move towards full-fledged Model-Based Systems Engineering (MBSE) Foreword by Rick Steiner xvii Foreword by Richard Soley xix Preface xxv Acknowledgments xxxi About the Author xxxiii Chapter 1: Overview of Model-Based Systems Engineering 1 1.1 What Is MBSE? 2 1.2 The Three Pillars of MBSE 4 1.3 The Myth of MBSE 9 Chapter 2: Overview of the Systems Modeling Language 11 2.1 What SysML Is?and Isn?t 11 2.2 Yes, SysML Is Based on UML?but You Can Start with SysML 13 2.3 SysML Diagram Overview 14 2.4 General Diagram Concepts 17 Chapter 3: Block Definition Diagrams 23 3.1 Purpose 23 3.2 When Should You Create a BDD? 24 3.3 The BDD Frame 24 3.4 Blocks 26 3.5 Associations: Another Notation for a Property 44 3.6 Generalizations 49 3.7 Dependencies 52 3.8 Actors 53 3.9 Value Types 55 3.10 Constraint Blocks 57 3.11 Comments 59 Chapter 4: Internal Block Diagrams 63 4.1 Purpose 63 4.2 When Should You Create an IBD? 64 4.3 Blocks, Revisited 64 4.4 The IBD Frame 65 4.5 BDDs and IBDs: Complementary Views of a Block 66