This book provides a practical approach to designing and implementing a Knowledge Management (KM) Strategy. The book explains how to design KM strategy so as to align business goals with KM objectives. The book also presents an approach for implementing KM strategy so as to make it sustainable. It covers all basic KM concepts, components of KM and the steps that are required for designing a KM strategy. As a result, the book can be used by beginners as well as practitioners. Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. These assets may include databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers. Knowledge is considered to be the learning that results from experience and is embedded within individuals. Sometimes the knowledge is gained through critical thinking, watching others, and observing results of others. These observations then form a pattern which is converted in a 'generic form' to knowledge. This implies that knowledge can be formed only after data (which is generated through experience or observation) is grouped into information and then this information pattern is made generic wisdom. However, dissemination and acceptance of this knowledge becomes a key factor in knowledge management. The knowledge pyramid represents the usual concept of knowledge transformations, where data is transformed into information, and information is transformed into knowledge. Many organizations have struggled to manage knowledge and translate it into business benefits. This book is an attempt to show them how it can be done.

Marking the 30th anniversary of the European Conference on Modelling and Simulation (ECMS), this inspirational text/reference reviews significant advances in the field of modelling and simulation, as well as key applications of simulation in other disciplines. The broad-ranging volume presents contributions from a varied selection of distinguished experts chosen from high-impact keynote speakers and best paper winners from the conference, including a Nobel Prize recipient, and the first president of the European Council for Modelling and Simulation (also abbreviated to ECMS). This authoritative book will be of great value to all researchers working in the field of modelling and simulation, in addition to scientists from other disciplines who make use of modelling and simulation approaches in their work.

This book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: To introduce when nonparametric approaches to data analysis are appropriate To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.

There are two different, interdependent components of IT that are important to a CIO: strategy, which is long-term; and tactical and operational concerns, which are short-term. Based on this distinction and its repercussions, this book clearly separates strategy from day-to-day operations and projects from operations - the two most important functions of a CIO. It starts by discussing the ideal organization of an IT department and the rationale behind it, and then goes on to debate the most pressing need - managing operations. It also explains some best industry standards and their practical implementation, and discusses project management, again highlighting the differences between the methodologies used in projects and those used in operations. A special chapter is devoted to the cutover of projects into operations, a critical aspect seldom discussed in detail. Other chapters touch on the management of IT portfolios, project governance, as well as agile project methodology, how it differs from the waterfall methodology, and when it is convenient to apply each. Taking the fundamental principles of IT service management and best practices in project management, the book offers a single, seamless reference for IT managers and professionals. It is highly practical, explaining how to apply these principles based on the author's extensive experience in industry.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

This book presents and discusses the state of the art and future trends in software engineering education, with a focus on agile methods and their budgetary implications. It introduces new and innovative methods, models and frameworks to focus the training towards the industry's requirements. The range of topics covered includes education models for software engineering, development of the software engineering discipline, innovation and evaluation of software engineering education, curricula for software engineering education, requirements and cultivation of outstanding software engineers for the future and cooperation models for industry and software engineering education.

This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pula (Cagliari), Italy, October 8-10, 2015.

This book accomplishes an analysis of critical aspects of managerial implications on the business with information. The business dealing with information is spreading in the service market; and, an efficient management of informational processes, in order to perform successful business with them, is now crucial. Besides, economical/business, technological or any other kind of information, organized in a variety of forms, can be considered as an 'informational product'. Thus, creating a business value out of information is challenging but vital, especially in the modern digital age. Accordingly, the book covers the methods and technologies to capture, integrate, analyze, mine, interpret and visualize information out of distributed data, which in turn can help to manage information competently. This volume explores the challenges being faced and opportunities to look out for in this research area, while discussing different aspects of this subject. The book will be of interest to those working in or are interested in joining interdisciplinary and transdisciplinary work in the areas of information management, service management, and service business. It will also be of use to young generation researchers by giving them an overview on different aspects of doing business with information. While introducing them to both technical and non-technical details, as well as economic aspects, the book will also be extremely informative for professionals who want to understand and realize the potential of using the cutting-edge managerial technologies for doing successful business with information/ services.

This book identifies, analyzes and discusses the current trends of digitalized, decentralized, and networked physical value creation by focusing on the particular example of 3D printing. In addition to evaluating 3D printing's disruptive potentials against a broader economic background, it also addresses the technology's potential impacts on sustainability and emerging modes of bottom-up and community-based innovation. Emphasizing these topics from economic, technical, social and environmental perspectives, the book offers a multifaceted overview that scrutinizes the scenario of a fundamental transition: from a centralized to a far more decentralized system of value creation.

This book presents a proposal for designing business process management (BPM) systems that comprise much more than just process modelling. Based on a purified Business Process Model and Notation (BPMN) variant, the authors present proposals for several important issues in BPM that have not been adequately considered in the BPMN 2.0 standard. It focusses on modality as well as actor and user interaction modelling and offers an enhanced communication concept. In order to render models executable, the semantics of the modelling language needs to be described rigorously enough to prevent deviating interpretations by different tools. For this reason, the semantics of the necessary concepts introduced in this book are defined using the Abstract State Machine (ASM) method. Finally, the authors show how the different parts of the model fit together using a simple example process, and introduce the enhanced Process Platform (eP2) architecture, which binds all the different components together. The resulting method is named Hagenberg Business Process Modelling (H-BPM) after the Austrian village where it was designed. The motivation for the development of the H-BPM method stems from several industrial projects in which business analysts and software developers struggled with redundancies and inconsistencies in system documentation due to missing integration. The book is aimed at researchers in business process management and industry 4.0 as well as advanced professionals in these areas.

This edited three volume edition brings together significant papers previously published in the Journal of information Technology (JIT) over its 30 year publication history. The three volumes of Enacting Research Methods in Information Systems celebrate the methodological pluralism used to advance our understanding of information technology's role in the world today. In addition to quantitative methods from the positivist tradition, JIT also values methodological articles from critical research perspectives, interpretive traditions, historical perspectives, grounded theory, and action research and design science approaches. Volume 1 covers Critical Research, Grounded Theory, and Historical Approaches. Volume 2 deals with Interpretive Approaches and also explores Action Research. Volume 3 focuses on Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research. The Journal of Information Technology (JIT) was started in 1986 by Professors Frank Land and Igor Aleksander with the aim of bringing technology and management together and bridging the 'great divide' between the two disciplines. The Journal was created with the vision of making the impact of complex interactions and developments in technology more accessible to a wider audience. Retaining this initial focus, the JIT has gone on to extend into new and innovative areas of research such as the launch of JITTC in 2010. A high impact journal, JIT shall continue to publish leading trends based on significant research in the field.

This edited three volume edition brings together significant papers previously published in the Journal of information Technology (JIT) over its 30 year publication history. The three volumes of Enacting Research Methods in Information Systems celebrate the methodological pluralism used to advance our understanding of information technology's role in the world today. In addition to quantitative methods from the positivist tradition, JIT also values methodological articles from critical research perspectives, interpretive traditions, historical perspectives, grounded theory, and action research and design science approaches. Volume 1 covers Critical Research, Grounded Theory, and Historical Approaches. Volume 2 deals with Interpretive Approaches and also explores Action Research. Volume 3 focuses on Design Science Approaches and discusses Alternative Approaches including Semiotics Research, Complexity Theory and Gender in IS Research. The Journal of Information Technology (JIT) was started in 1986 by Professors Frank Land and Igor Aleksander with the aim of bringing technology and management together and bridging the 'great divide' between the two disciplines. The Journal was created with the vision of making the impact of complex interactions and developments in technology more accessible to a wider audience. Retaining this initial focus, the JIT has gone on to extend into new and innovative areas of research such as the launch of JITTC in 2010. A high impact journal, JIT shall continue to publish leading trends based on significant research in the field.

This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

This textbook examines empirical linguistics from a theoretical linguist's perspective. It provides both a theoretical discussion of what quantitative corpus linguistics entails and detailed, hands-on, step-by-step instructions to implement the techniques in the field. The statistical methodology and R-based coding from this book teach readers the basic and then more advanced skills to work with large data sets in their linguistics research and studies. Massive data sets are now more than ever the basis for work that ranges from usage-based linguistics to the far reaches of applied linguistics. This book presents much of the methodology in a corpus-based approach. However, the corpus-based methods in this book are also essential components of recent developments in sociolinguistics, historical linguistics, computational linguistics, and psycholinguistics. Material from the book will also be appealing to researchers in digital humanities and the many non-linguistic fields that use textual data analysis and text-based sensorimetrics. Chapters cover topics including corpus processing, frequencing data, and clustering methods. Case studies illustrate each chapter with accompanying data sets, R code, and exercises for use by readers. This book may be used in advanced undergraduate courses, graduate courses, and self-study.

This book is published under a CC BY-NC 4.0 license. The editors present essential methods and tools to support a holistic approach to the challenge of system upgrades and innovation in the context of high-value products and services. The approach presented here is based on three main pillars: an adaptation mechanism based on a broad understanding of system dependencies; efficient use of system knowledge through involvement of actors throughout the process; and technological solutions to enable efficient actor communication and information handling. The book provides readers with a better understanding of the factors that influence decisions, and put forward solutions to facilitate the rapid adaptation to changes in the business environment and customer needs through intelligent upgrade interventions. Further, it examines a number of sample cases from various contexts including car manufacturing, utilities, shipping and the furniture industry. The book offers a valuable resource for both academics and practitioners interested in the upgrading of capital-intensive products and services. "The work performed in the project "Use-It-Wisely (UiW)" significantly contributes towards a collaborative way of working. Moreover, it offers comprehensive system modelling to identify business opportunities and develop technical solutions within industrial value networks. The developed UiW-framework fills a void and offers a great opportunity. The naval construction sector of small passenger vessels, for instance, is one industry that can benefit." Nikitas Nikitakos, Professor at University of the Aegean, Department of Shipping, Trade, and Transport, Greece. "Long-life assets are crucial for both the future competiveness and sustainability of society. Make wrong choices now and you are locked into a wrong system for a long time. Make the right choices now and society can prosper. This book gives important information about how manufacturers can make right choices." Arnold Tukker, Scientific director, Institute of Environmental Sciences (CML), Leiden University, and senior scientist, TNO.

This book examines trends and challenges in research on IT governance in public organizations, reporting innovative research and new insights in the theories, models and practices within the area. As we noticed, IT governance plays an important role in generating value from organization's IT investments. However there are different challenges for researchers in studying IT governance in public organizations due to the differences between political, administrative, and practices in these organizations. The first section of the book looks at Management issues, including an introduction to IT governance in public organizations; a systematic review of IT alignment research in public organizations; the role of middle managers in aligning strategy and IT in public service organizations; and an analysis of alignment and governance with regard to IT-related policy decisions. The second section examines Modelling, including a consideration of the challenges faced by public administration; a discussion of a framework for IT governance implementation suitable to improve alignment and communication between stakeholders of IT services; the design and implementation of IT architecture; and the adoption of enterprise architecture in public organizations. Finally, section three presents Case Studies, including IT governance in the context of e-government strategy implementation in the Caribbean; the relationship of IT organizational structure and IT governance performance in the IT department of a public research and education organization in a developing country; the relationship between organizational ambidexterity and IT governance through a study of the Swedish Tax Authorities; and the role of institutional logics in IT project activities and interactions in a large Swedish hospital.

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 book analyses quantitative open source software (OSS) reliability assessment and its applications, focusing on three major topic areas: the Fundamentals of OSS Quality/Reliability Measurement and Assessment; the Practical Applications of OSS Reliability Modelling; and Recent Developments in OSS Reliability Modelling. Offering an ideal reference guide for graduate students and researchers in reliability for open source software (OSS) and modelling, the book introduces several methods of reliability assessment for OSS including component-oriented reliability analysis based on analytic hierarchy process (AHP), analytic network process (ANP), and non-homogeneous Poisson process (NHPP) models, the stochastic differential equation models and hazard rate models. These measurement and management technologies are essential to producing and maintaining quality/reliable systems using OSS.

This book reports on the latest advances and applications of chaotic systems. It consists of 25 contributed chapters by experts who are specialized in the various topics addressed in this book. The chapters cover a broad range of topics of chaotic systems such as chaos, hyperchaos, jerk systems, hyperjerk systems, conservative and dissipative systems, circulant chaotic systems, multi-scroll chaotic systems, finance chaotic system, highly chaotic systems, chaos control, chaos synchronization, circuit realization and applications of chaos theory in secure communications, mobile robot, memristors, cellular neural networks, etc. Special importance was given to chapters offering practical solutions, modeling and novel control methods for the recent research problems in chaos theory. This book will serve as a reference book for graduate students and researchers with a basic knowledge of chaos theory and control systems. The resulting design procedures on the chaotic systems are emphasized using MATLAB software.

This book identifies and discusses the main challenges facing digital business innovation and the emerging trends and practices that will define its future. The book is divided into three sections covering trends in digital systems, digital management, and digital innovation. The opening chapters consider the issues associated with machine intelligence, wearable technology, digital currencies, and distributed ledgers as their relevance for business grows. Furthermore, the strategic role of data visualization and trends in digital security are extensively discussed. The subsequent section on digital management focuses on the impact of neuroscience on the management of information systems, the role of IT ambidexterity in managing digital transformation, and the way in which IT alignment is being reconfigured by digital business. Finally, examples of digital innovation in practice at the global level are presented and reviewed. The book will appeal to both practitioners and academics. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox that enables easy understanding and assists in exploiting business opportunities involving digital business innovation.