Studienarbeit aus dem Jahr 2018 im Fachbereich Informatik - Wirtschaftsinformatik, Note: 1,7, Hochschule Aalen, Veranstaltung: International Project Management, Sprache: Deutsch, Abstract: Wir bewegen uns im Zeitalter der Industrie 4.0, die die Unternehmen mit modernster Informations- und Kommunikationstechnik verzahnt. Der größte Beweggrund dieser Entwicklung ist die schnell zunehmende Digitalisierung der Wirtschaft. Ein populärer Anwendungsbereich der Industrie 4.0 ist Predictive Maintenance. Predictive Maintenance wird als ein Schlüsselthema in der Industrie 4.0 identifiziert und als eindeutige Voraussetzung für zukünftigen Erfolg in der Wartung gesehen. Der Begriff ´´Predictive Maintenance´´ (PM) lässt sich mit dem Begriff ´´vorausschauende Wartung´´ ins Deutsche übersetzen. Das Ziel von PM besteht darin, Zustandsdaten von technischen Anlagen zur Vorhersage von möglichen Ausfällen der Maschinen sowie Maschinenteilen zu nutzen. Insbesondere für produzierende Unternehmen ist PM vongroßer Bedeutung, da diese überwiegend für ihre Wartungsarbeiten PM einsetzen, allerdings nur für bekannte oder vermutete Ursache-Wirkungs-Zusammenhänge. Nur wenn der Technologie bekannt ist, dass eine Änderung der Charakteristik auf einen sich anbahnenden Schaden hinweist, kann man die Anlagen im Sinne einer vorausschauenden Wartung nutzen. PM wird daher aktuell noch recht unkritisch betrieben. Defizite herrschen hinsichtlich der zuverlässigen Quantifizierung von Instandhaltung sowie notwendigen Instandhaltungsmaßnahmen. Daher kann auch der wirtschaftliche Erfolg von PM nur geschätzt werden, und zwar aufgrund der Weiterentwicklung von Technik aber auch unterschiedlicher äußerer Einflussfaktoren, die nie komplett ausgeblendet beziehungsweise herausgerechnet werden können. Alles in allem kann man also festhalten, dass weder bewiesen ist, dass PM tatsächlich zu Einsparungspotenzialen führt noch, dass mithilfe von PM überhaupt in die Zukunft geblickt werden kann, d.h. Vorhersagen über das zukünftige Verhalten einer Maschine getroffen werden und somit Wartungen optimiert werden können. PM ist nur ein Begriff, welcher eine Vorhersage verspricht, jedoch ist weder in der Realität noch durch die Technologie ein Blick in die Zukunft möglich. Zielsetzung dieser Arbeit ist es somit zu ermitteln, ob PM durch die Zukunftsbezogenheit in der Praxis funktioniert und wie gegebenenfalls der Erfolg in der Praxis konkret gemessen wird.
Automate your workload and manage more databases and instances with greater ease and efficiency by combining metadata-driven automation with powerful tools like PowerShell and SQL Server Agent. Automate your new instance-builds and use monitoring to drive ongoing automation, with the help of an inventory database and a management data warehouse. The market has seen a trend towards there being a much smaller ratio of DBAs to SQL Server instances. Automation is the key to responding to this challenge and continuing to run a reliable database platform service. Expert Scripting and Automation for SQL Server DBAs guides you through the process of automating the maintenance of your SQL Server enterprise. Expert Scripting and Automation for SQL Server DBAs shows how to automate the SQL Server build processes, monitor multiple instances from a single location, and automate routine maintenance tasks throughout your environment. You will also learn how to create automated responses to common or time consuming break/fix scenarios. The book helps you become faster and better at what you do for a living, and thus more valuable in the job market. Extensive coverage of automation using PowerShell and T-SQL Detailed discussion and examples on metadata-driven automation Comprehensive coverage of automated responses to break/fix scenarios What You Will Learn Automate the SQL Server build process Create intelligent, metadata-drive routines Automate common maintenance tasks Create automated responses to common break/fix scenarios Monitor multiple instance from a central location Utilize T-SQL and PowerShell for administrative purposes Who This Book Is For Expert Scripting and Automation for SQL Server DBAs is a book for SQL Server database administrators responsible for managing increasingly large numbers of databases across their business enterprise. The book is also useful for any database administrator looking to ease their workload through automation. The book addresses the needs of these audiences by showing how to get more done through less effort by implementing an intelligent, automated-processes service model using tools such as T-SQL, PowerShell, Server Agent, and the Management Data Warehouse.
CMMI® for Development (CMMI-DEV) describes best practices for the development and maintenance of products and services across their entire lifecycle. By integrating essential bodies of knowledge, CMMI-DEV provides a single, comprehensive framework for organizations to assess their development and maintenance processes, implement improvements, and measure progress. Already widely adopted throughout the world for disciplined, high-quality engineering, CMMI-DEV version 1.3 now accommodates other modern approaches as well, including the use of Agile methods, Lean Six Sigma, and architecture-related development. CMMI® for Development, Third Edition, is the definitive reference for CMMIDEV version 1.3. The authors have revised their tips, hints, and cross-references, which appear in the margins of the book, to help you better understand, apply, and find information about the content of each process area. The book includes new and updated perspectives on CMMI-DEV in which people influential in the model´s creation, development, and transition share brief but valuable insights. It also features four new case studies and six contributed essays with practical advice for adopting and using CMMI-DEV. This book is an essential resource-whether you are new to CMMI-DEV or are familiar with an earlier version-if you need to know about, evaluate, or put the latest version of the model into practice. The book is divided into three parts. Part I offers the broad view of CMMI-DEV, beginning with basic concepts of process improvement. It introduces the process areas, their components, and their relationships to each other. It describes effective paths to the adoption and use of CMMI-DEV for process improvement and benchmarking, all illuminated with fresh case studies and helpful essays. Part II, the bulk of the book, details the generic goals and practices and the twenty-two process areas now comprising CMMI-DEV. The process areas are organized alphabetically by acronym for easy reference. Each process area includes goals, best practices, and examples. Part III contains several useful resources, including CMMI-DEV-related references, acronym definitions, a glossary of terms, and an index. Product Description CMMI® for Development (CMMI-DEV) describes best practices for the development and maintenance of products and services across their lifecycle. By integrating essential bodies of knowledge, CMMI-DEV provides a single, comprehensive framework for organizations to assess their development and maintenance processes and improve performance. Already widely adopted throughout the world for disciplined, high-quality engineering, CMMI-DEV Version 1.3 now accommodates other modern approaches as well, including the use of Agile methods, Lean Six Sigma, and architecture-centric development. CMMI® for Development, Third Edition, is the definitive reference for CMMI-DEV Version 1.3. The authors have revised their tips, hints, and cross-references, which appear in the margins of the book, to help you better understand, apply, and find information about the content of each process area. The book includes new and updated perspectives on CMMI-DEV in which people influential in the model´s creation, development, and transition share brief but valuable insights. It also features four new case studies and five contributed essays with practical advice for adopting and using CMMI-DEV. This book is an essential resource-whether you are new to CMMI-DEV or are familiar with an earlier version-if you need to know about, evaluate, or put the latest version of the model into practice. The book is divided into three parts. Part One offers the broad view of CMMI-DEV, beginning with basic concepts of process improvement. It introduces the process areas, their components, and their relationships to each other. It describes effective paths to the adoption and use of CMMI-DEV for process improvement and benchmarking, all illuminated with fresh case studies and helpful essays. Part Two, the bulk of the book, details the generic goals and practices and the twenty-two process areas now comprising CMMI-DEV. The process areas are organized alphabetically by acronym for easy reference. Each process area includes goals, best practices, and examples. Part Three contains several useful resources, including CMMI-DEV-related references, acronym definitions, a glossary of terms, and an index. List of Perspectives xiii Preface xv Book Acknowledgments xxi Part One: About CMMI for
TOGAF is a framework - a detailed method and a set of supporting tools - for developing an enterprise architecture, developed by members of The Open Group Architecture Forum. TOGAF Version 9.1 is a maintenance update to TOGAF 9, addressing comments raised since the introduction of TOGAF 9 in 2009. It retains the major features and structure of TOGAF 9, thereby preserving existing investment in TOGAF, and adds further detail and clarification to what is already proven.It may be used freely by any organization wishing to develop an enterprise architecture for use within that organization (subject to the Conditions of Use). This Book is divided into seven parts: Part I - Introduction This part provides a high-level introduction to the key concepts of enterprise architecture and in particular the TOGAF approach. It contains the definitions of terms used throughout TOGAF and release notes detailing the changes between this version and the previous version of TOGAF. Part II - Architecture Development Method This is the core of TOGAF. It describes the TOGAF Architecture Development Method (ADM) a step-by-step approach to developing an enterprise architecture. Part III - ADM Guidelines & Techniques This part contains a collection of guidelines and techniques available for use in applying TOGAF and the TOGAF ADM. Part IV - Architecture Content Framework This part describes the TOGAF content framework, including a structured metamodel for architectural artifacts, the use of re-usable architecture building blocks, and an overview of typical architecture deliverables. Part V - Enterprise Continuum & Tools This part discusses appropriate taxonomies and tools to categorize and store the outputs of architecture activity within an enterprise. Part VI - TOGAF Reference Models This part provides a selection of architectural reference models, which includes the TOGAF Foundation Architecture, and the Integrated Information Infrastructure Reference Model (III-RM). Part VII Architecture Capability Framework This section looks at roles, Governance, compliance skills and much more practical guidance
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What´s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration - a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace
Data Science steht derzeit wie kein anderer Begriff für die Auswertung großer Datenmengen mit analytischen Konzepten des Machine Learning oder der künstlichen Intelligenz. Nach der bewussten Wahrnehmung der Big Data und dabei insbesondere der Verfügbarmachung in Unternehmen sind Technologien und Methoden zur Auswertung dort gefordert, wo klassische Business Intelligence an ihre Grenzen stößt. Dieses Buch bietet eine umfassende Einführung in Data Science und deren praktische Relevanz für Unternehmen. Dabei wird auch die Integration von Data Science in ein bereits bestehendes Business-Intelligence-Ökosystem thematisiert. In verschiedenen Beiträgen werden sowohl Aufgabenfelder und Methoden als auch Rollen- und Organisationsmodelle erläutert, die im Zusammenspiel mit Konzepten und Architekturen auf Data Science wirken. Neben den Grundlagen werden unter anderem folgende Themen behandelt: - Data Science und künstliche Intelligenz - Konzeption und Entwicklung von Data-driven Products - Deep Learning - Self-Service im Data-Science-Umfeld - Data Privacy und Fragen zur digitalen Ethik - Customer Churn mit Keras/TensorFlow und H2O - Wirtschaftlichkeitsbetrachtung bei der Auswahl und Entwicklung von Data Science - Predictive Maintenance - Scrum in Data-Science-Projekten Zahlreiche Anwendungsfälle und Praxisbeispiele geben Einblicke in die aktuellen Erfahrungen bei Data-Science-Projekten und erlauben dem Leser einen direkten Transfer in die tägliche Arbeit.
While standardization has empowered the software industry to substantially scale software development and to provide affordable software to a broad market, it often does not address smaller market segments, nor the needs and wishes of individual customers. Software product lines reconcile mass production and standardization with mass customization in software engineering. Ideally, based on a set of reusable parts, a software manufacturer can generate a software product based on the requirements of its customer. The concept of features is central to achieving this level of automation, because features bridge the gap between the requirements the customer has and the functionality a product provides. Thus features are a central concept in all phases of product-line development. The authors take a developer´s viewpoint, focus on the development, maintenance, and implementation of product-line variability, and especially concentrate on automated product derivation based on a user´s feature selection. The book consists of three parts. Part I provides a general introduction to feature-oriented software product lines, describing the product-line approach and introducing the product-line development process with its two elements of domain and application engineering. The pivotal part II covers a wide variety of implementation techniques including design patterns, frameworks, components, feature-oriented programming, and aspect-oriented programming, as well as tool-based approaches including preprocessors, build systems, version-control systems, and virtual separation of concerns. Finally, part III is devoted to advanced topics related to feature-oriented product lines like refactoring, feature interaction, and analysis tools specific to product lines. In addition, an appendix lists various helpful tools for software product-line development, along with a description of how they relate to the topics covered in this book. To tie the book together, the authors use two running examples that are well documented in the product-line literature: data management for embedded systems, and variations of graph data structures. They start every chapter by explicitly stating the respective learning goals and finish it with a set of exercises; additional teaching material is also available online. All these features make the book ideally suited for teaching - both for academic classes and for professionals interested in self-study.
Testing at Google Scale Product Description 2012 Jolt Award finalist! Pioneering the Future of Software Test Do you need to get it right, too? Then, learn from Google . Legendary testing expert James Whittaker, until recently a Google testing leader, and two top Google experts reveal exactly how Google tests software, offering brand-new best practices you can use even if you?re not quite Google?s size? yet! Breakthrough Techniques You Can Actually Use Discover 100% practical, amazingly scalable techniques for analyzing risk and planning tests?thinking like real users?implementing exploratory, black box, white box, and acceptance testing?getting usable feedback?tracking issues?choosing and creating tools?testing ?Docs & Mocks,? interfaces, classes, modules, libraries, binaries, services, and infrastructure?reviewing code and refactoring?using test hooks, presubmit scripts, queues, continuous builds, and more. With these techniques, you can transform testing from a bottleneck into an accelerator ?and make your whole organization more productive! Features + Benefits Presents pioneering testing techniques that can help any company moving to the cloud Shows how to achieve web-level scale for integration and system testing Offers expert guidance on managing end-to-end testing, including superior automation strategies Foreword by Alberto Savoia xiii Foreword by Patrick Copeland xvii Preface xxiii Chapter 1: Introduction to Google Software Testing 1 Quality?Test 5 Roles 6 Organizational Structure 8 Crawl, Walk, Run 10 Types of Tests 12 Chapter 2: The Software Engineer in Test 15 The Life of an SET 17 Development and Test Workflow 17 Who Are These SETs Anyway? 22 The Early Phase of a Project 22 Team Structure 24 Design Docs 25 Interfaces and Protocols 27 Automation Planning 28 Testability 29 SET Workflow: An Example 32 Test Execution 40 Test Size Definitions 41 Use of Test Sizes in Shared Infrastructure 44 Benefits of Test Sizes 46 Test Runtime Requirements 48 Case 1: Change in Common Library 52 Test Certified 54 An Interview with the Founders of the Test Certified Program 57 Interviewing SETs 62 An Interview with Tool Developer Ted Mao 68 An Interview with Web Driver Creator Simon Stewart 70 Chapter 3: The Test Engineer 75 A User-Facing Test Role 75 The Life of a TE 76 Test Planning 79 Risk 97 Life of a Test Case 108 Life of a Bug 113 Recruiting TEs 127 Test Leadership at Google 134 Maintenance Mode Testing 137 Quality Bots Experiment 141 BITE Experiment 153 Google Test Analytics 163 Free Testing Workflow 169 External Vendors 173 An Interview with Google Docs TE Lindsay Webster 175 An Interview with YouTube TE Apple Chow 181 Chapter 4: The Test Engineering Manager 187 The Life of a TEM 187 Getting Projects and People 189 Impact 191 An Interview with Gmail TEM Ankit Mehta 193 An Interview with Android TEM Hung Dang 198 An Interview with Chrome TEM Joel Hynoski 202 The Test Engineering Director 206 An Interview with Search and Geo Test Director Shelton Mar 207 An Interview with Engineering Tools Director Ashish Kumar 211 An Interview with Google India Test Director Sujay Sahni 214 An Interview with Engineering Manager Brad Green 219 An Interview with James Whittaker 222 Chapter 5: Improving How Google Tests Software 229 Fatal Flaws in Google´s Process 229 The Future of the SET 231 The Future of the TE 233 The Future of the Test Director and Manager 234 The Future of Test Infrastructure 234 In Conclusion 235 Appendix A: Chrome OS Test Plan 237 Overview of Themes 237 Risk Analysis 238 Per-Build Baseline Testing 239 Per-LKG Day Testing 239 Per-Release Testing 239 Manual Versus Automation 240 Dev Versus Test Quality Focus 240 Release Channels 240 User Input 241 Test Case Repositories 241 Test Dashboarding 241 Virtualization 241 Performance 242 Stress, Long-Running, and Stability 242 Test Execution Framework (Autotest) 242 OEMs 242 Hardware Lab 242 E2E Farm Automation 243 Testing the Browser AppManager 243 Browser Testability 243 Hardware 244 Timeline 244 Primary Test Drivers 246 Relevant Documents 246 Appendix B: Test Tours for Chrome 247 The Shopping Tour 247 The Student Tour 248 Suggested Areas to Test 248 The International Calling Tour 249 Suggested Areas to Test 249 The Landmark Tour 249 Suggested Landmarks in Chrome 249 The All Nighter Tour 250 Suggested Areas to Test 250 The Artisan´s Tour 251 Tools in Chrome 251 The Bad Neighborhood Tour 251 Bad Neighborhoods in Chrome OS 251 The Personalization Tour 252 Ways to Customize
The authors are renowned experts on the topic of testing in agile environments. They have remained very active and accessible in both the agile and testing communities since the publication of their first book. This shorter book supplements the lessons of its predecessor, and provides even more practical advice on how to successfully implement and manage a testing program in an agile setting. The book further defines agile testing and illustrates the tester?s role with contemporary examples from real agile teams. This book is another must for agile testers, agile teams, their managers, and their customers. Product Description Janet Gregory and Lisa Crispin pioneered the agile testing discipline with their previous work, Agile Testing . Now, in More Agile Testing, they reflect on all they?ve learned since. They address crucial emerging issues, share evolved agile practices, and cover key issues agile testers have asked to learn more about. Packed with new examples from real teams, this insightful guide offers detailed information about adapting agile testing for your environment; learning from experience and continually improving your test processes; scaling agile testing across teams; and overcoming the pitfalls of automated testing. You?ll find brand-new coverage of agile testing for the enterprise, distributed teams, mobile/embedded systems, regulated environments, data warehouse/BI systems, and DevOps practices. You?ll come away understanding ? How to clarify testing activities within the team ? Ways to collaborate with business experts to identify valuable features and deliver the right capabilities ? How to design automated tests for superior reliability and easier maintenance ? How agile team members can improve and expand their testing skills ? How to plan ?just enough,? balancing small increments with larger feature sets and the entire system ? How to use testing to identify and mitigate risks associated with your current agile processes and to prevent defects ? How to address challenges within your product or organizational context ? How to perform exploratory testing using ?personas? and ?tours? ? Exploratory testing approaches that engage the whole team, using test charters with session- and thread-based techniques ? How to bring new agile testers up to speed quickly?without overwhelming them The eBook edition of More Agile Testing also is available as part of a two-eBook collection, The Agile Testing Collection (9780134190624). Features + Benefits Codifies the latest thinking on testing for agile projects and builds upon the feedback received from the authors´ previous book Readers will come away from this book understanding how to get testers engaged in the agile development process Shows where testers and QA managers fit into the equation, and how the development and testing teams can work hand-in-hand on an agile project Another addition to the highly successful Mike Cohn Signature Series Foreword by Elisabeth Hendrickson xvii Foreword by Johanna Rothman xix Preface xxi Acknowledgments xxix About the Authors xxxiii About the Contributors xxxv Part I: Introduction 1 Chapter 1: How Agile Testing Has Evolved 3 Summary 6 Chapter 2: The Importance of Organizational Culture 7 Investing Time 8 The Importance of a Learning Culture 12 Fostering a Learning Culture 13 Transparency and Feedback Loops 15 Educating the Organization 17 Managing Testers 19 Summary 20 Part II: Learning for Better Testing 21 Chapter 3: Roles and Competencies 23 Competencies versus Roles 24 T-Shaped Skill Set 28 Generalizing Specialists 33 Hiring the Right People 36 Onboarding Testers 37 Summary 39 Chapter 4: Thinking Skills for Testing 41 Facilitating 42 Solving Problems 43 Giving and Receiving Feedback 45 Learning the Business Domain 46 Coaching and Listening Skills 48 Thinking Differently 49 Organizing 51 Collaborating 52 Summary 53 Chapter 5: Technical Awareness 55 Guiding Development with Examples 55 Automation and Coding Skills 56 General Technical Skills 59 Development Environments 59 Test Environments 60 Continuous Integration and Source Code Control Systems 62 Testing Quality Attributes 65 Test Design Techniques 67 Summary 67 Chapter 6: How to Learn 69 Learning Styles 69 Learning Resources 72 Time for Learning 77 Helping Others Learn 79 Summary 83 Part III: Planning?So You Don?t Forget the Big Picture 85 Chapter 7: Levels of Precision for Planning 87 Different Points of View 87 Planning for Regression Testing 97 Visualize What