Información de una colección
|
|
Documentos disponibles dentro de esta colección (4)
Refinar búsquedaAdvances in Research Methods for Information Systems Research / SpringerLink (Online service) ; Osei-Bryson, Kweku-Muata ; Ngwenyama, Ojelanki (2014)
Título : Advances in Research Methods for Information Systems Research : Data Mining, Data Envelopment Analysis, Value Focused Thinking Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Osei-Bryson, Kweku-Muata ; Ngwenyama, Ojelanki Editorial: Boston, MA : Springer US Fecha de publicación: 2014 Otro editor: Imprint: Springer Colección: Integrated Series in Information Systems, ISSN 1571-0270 num. 34 Número de páginas: VII, 231 p. 52 illus., 30 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4614-9463-8 Idioma : Inglés (eng) Palabras clave: Business Information technology Data processing Computers mining and Management IT in Mining Knowledge Discovery Systems Communication Service Clasificación: 658 Empresas. Organización de empresas Resumen: Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science Nota de contenido: Introduction -- Logical Foundations of Social Science Research -- Overview on Decision Tree Induction -- Using Decision Tree Induction for Theory Development -- A Hybrid Decision Tree-based Method for Exploring Cumulative Abnormal Returns -- An Ethnographic Decision Tree Modeling: An Exploration of Telecentre Usage in the Human Development Context -- Using Association Rules Mining to Facilitate Qualitative Data Analysis in Theory Building -- Overview on Multivariate Adaptive Regression Splines -- Reexamining the Impact of Information Technology Investment on Productivity Using Regression Tree and MARS -- Overview on Cluster Analysis -- Overview on Data Envelopment Analysis -- Exploring the ICT Utilization using Data Envelopment Analysis -- A DEA-centric Decision Support System for Monitoring Efficiency-Based Performance -- Overview on the Value Focused Thinking Methodology -- Using Value Focused Thinking to Develop Performance Criteria & Measures for Information Systems Projects En línea: http://dx.doi.org/10.1007/978-1-4614-9463-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35928 Advances in Research Methods for Information Systems Research : Data Mining, Data Envelopment Analysis, Value Focused Thinking [documento electrónico] / SpringerLink (Online service) ; Osei-Bryson, Kweku-Muata ; Ngwenyama, Ojelanki . - Boston, MA : Springer US : Imprint: Springer, 2014 . - VII, 231 p. 52 illus., 30 illus. in color : online resource. - (Integrated Series in Information Systems, ISSN 1571-0270; 34) .
ISBN : 978-1-4614-9463-8
Idioma : Inglés (eng)
Palabras clave: Business Information technology Data processing Computers mining and Management IT in Mining Knowledge Discovery Systems Communication Service Clasificación: 658 Empresas. Organización de empresas Resumen: Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science Nota de contenido: Introduction -- Logical Foundations of Social Science Research -- Overview on Decision Tree Induction -- Using Decision Tree Induction for Theory Development -- A Hybrid Decision Tree-based Method for Exploring Cumulative Abnormal Returns -- An Ethnographic Decision Tree Modeling: An Exploration of Telecentre Usage in the Human Development Context -- Using Association Rules Mining to Facilitate Qualitative Data Analysis in Theory Building -- Overview on Multivariate Adaptive Regression Splines -- Reexamining the Impact of Information Technology Investment on Productivity Using Regression Tree and MARS -- Overview on Cluster Analysis -- Overview on Data Envelopment Analysis -- Exploring the ICT Utilization using Data Envelopment Analysis -- A DEA-centric Decision Support System for Monitoring Efficiency-Based Performance -- Overview on the Value Focused Thinking Methodology -- Using Value Focused Thinking to Develop Performance Criteria & Measures for Information Systems Projects En línea: http://dx.doi.org/10.1007/978-1-4614-9463-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35928 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Knowledge Management for Development : Domains, Strategies and Technologies for Developing Countries Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Osei-Bryson, Kweku-Muata ; Mansingh, Gunjan ; Rao, Lila Editorial: Boston, MA : Springer US Fecha de publicación: 2014 Otro editor: Imprint: Springer Colección: Integrated Series in Information Systems, ISSN 1571-0270 num. 35 Número de páginas: XXVIII, 270 p. 39 illus., 16 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4899-7392-4 Idioma : Inglés (eng) Palabras clave: Business Globalization Markets Computers Economics and Management Emerging Markets/Globalization Information Systems Communication Service Economic Clasificación: 658.011.8 Gestión del conocimiento. Innovación y mejoras en las organizaciones Resumen: A number of developing countries, including small island states, have common problems that have affected their development and growth. Knowledge Management (KM) initiatives can be used to address some of these issues, but these developing countries need to understand what is needed to implement them, in order to improve economic conditions. While many of these countries have access to technologies that can be used to assist in knowledge management, relevant and low cost KM initiatives need to be considered in improving their existing KM processes. Sectors critical to the growth of these developing countries include health care, agriculture, disaster recovery management, small and medium-size enterprise development. Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries highlights the opportunities in these sectors and provides advice as to how these countries should go about understanding, building and adopting the relevant KM strategies and technologies. This book identifies appropriate technologies which should be considered to increase productivity within the identified sectors in the developing countries, and also sectors in which knowledge management initiatives can yield maximum value. It also considers the constraints of these territories, recommending appropriate technologies and strategies for KM initiatives. It provides advice on how these technologies should be adopted in these sectors of developing countries. Investing in these strategies should benefit these countries development and growth Nota de contenido: Chapter 1 Towards Understanding and Applying Knowledge Management and Knowledge Management Systems in Developing Countries: Some Conceptual Foundations -- Chapter 2 Towards a Community-Centered Knowledge Management Architecture for Disaster Management in Sub Saharan Africa -- Chapter 3 Managing Expert Knowledge to Assist in the Management of Coffee Pests and Diseases in Jamaica -- Chapter 4 Implementation of a Multiagent Supervisory System for an Agricultural Products Sourcing Network -- Chapter 5 Progressive Usage of Business and Spatial Intelligence for Decision Support in the Delivery of Educational Services in Developing Countries -- Chapter 6 Migrating MIS to KMS: A Case of Social Welfare Systems -- Chapter 7 Addressing a Knowledge Externality Schism in Public Policy in the English Speaking Caribbean -- Chapter 8 A Methodology for Developing High Quality Ontologies for Knowledge Management -- Chapter 9 The Role of Ontologies in Developing Knowledge Technologies -- Chapter 10 Knowledge Sharing in Repository Based KM Systems: A Study in the IT Services Enterprises in India -- Chapter 11 Ability to Share Knowledge of Doctors in Teaching Hospitals in Indonesia -- Chapter 12 Knowledge sharing in the Health Sector in Jamaica: The Barriers and the Enablers -- Chapter 13 The Ulwazi Programme: A Case Study in Community-focused Indigenous Knowledge Management -- Chapter 14 Knowledge Management for Programs on Information and Communications Technologies for Development (ICT4D) in South Africa En línea: http://dx.doi.org/10.1007/978-1-4899-7392-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35938 Knowledge Management for Development : Domains, Strategies and Technologies for Developing Countries [documento electrónico] / SpringerLink (Online service) ; Osei-Bryson, Kweku-Muata ; Mansingh, Gunjan ; Rao, Lila . - Boston, MA : Springer US : Imprint: Springer, 2014 . - XXVIII, 270 p. 39 illus., 16 illus. in color : online resource. - (Integrated Series in Information Systems, ISSN 1571-0270; 35) .
ISBN : 978-1-4899-7392-4
Idioma : Inglés (eng)
Palabras clave: Business Globalization Markets Computers Economics and Management Emerging Markets/Globalization Information Systems Communication Service Economic Clasificación: 658.011.8 Gestión del conocimiento. Innovación y mejoras en las organizaciones Resumen: A number of developing countries, including small island states, have common problems that have affected their development and growth. Knowledge Management (KM) initiatives can be used to address some of these issues, but these developing countries need to understand what is needed to implement them, in order to improve economic conditions. While many of these countries have access to technologies that can be used to assist in knowledge management, relevant and low cost KM initiatives need to be considered in improving their existing KM processes. Sectors critical to the growth of these developing countries include health care, agriculture, disaster recovery management, small and medium-size enterprise development. Knowledge Management for Development: Domains, Strategies and Technologies for Developing Countries highlights the opportunities in these sectors and provides advice as to how these countries should go about understanding, building and adopting the relevant KM strategies and technologies. This book identifies appropriate technologies which should be considered to increase productivity within the identified sectors in the developing countries, and also sectors in which knowledge management initiatives can yield maximum value. It also considers the constraints of these territories, recommending appropriate technologies and strategies for KM initiatives. It provides advice on how these technologies should be adopted in these sectors of developing countries. Investing in these strategies should benefit these countries development and growth Nota de contenido: Chapter 1 Towards Understanding and Applying Knowledge Management and Knowledge Management Systems in Developing Countries: Some Conceptual Foundations -- Chapter 2 Towards a Community-Centered Knowledge Management Architecture for Disaster Management in Sub Saharan Africa -- Chapter 3 Managing Expert Knowledge to Assist in the Management of Coffee Pests and Diseases in Jamaica -- Chapter 4 Implementation of a Multiagent Supervisory System for an Agricultural Products Sourcing Network -- Chapter 5 Progressive Usage of Business and Spatial Intelligence for Decision Support in the Delivery of Educational Services in Developing Countries -- Chapter 6 Migrating MIS to KMS: A Case of Social Welfare Systems -- Chapter 7 Addressing a Knowledge Externality Schism in Public Policy in the English Speaking Caribbean -- Chapter 8 A Methodology for Developing High Quality Ontologies for Knowledge Management -- Chapter 9 The Role of Ontologies in Developing Knowledge Technologies -- Chapter 10 Knowledge Sharing in Repository Based KM Systems: A Study in the IT Services Enterprises in India -- Chapter 11 Ability to Share Knowledge of Doctors in Teaching Hospitals in Indonesia -- Chapter 12 Knowledge sharing in the Health Sector in Jamaica: The Barriers and the Enablers -- Chapter 13 The Ulwazi Programme: A Case Study in Community-focused Indigenous Knowledge Management -- Chapter 14 Knowledge Management for Programs on Information and Communications Technologies for Development (ICT4D) in South Africa En línea: http://dx.doi.org/10.1007/978-1-4899-7392-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35938 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Machine Learning Models and Algorithms for Big Data Classification : Thinking with Examples for Effective Learning Tipo de documento: documento electrónico Autores: Suthaharan, Shan ; SpringerLink (Online service) Editorial: Boston, MA : Springer US Fecha de publicación: 2016 Otro editor: Imprint: Springer Colección: Integrated Series in Information Systems, ISSN 1571-0270 num. 36 Número de páginas: XIX, 359 p. 149 illus., 82 illus. in color Il.: online resource ISBN/ISSN/DL: 978-1-4899-7641-3 Idioma : Inglés (eng) Palabras clave: Business Management Database management Artificial intelligence and Intelligence (incl. Robotics) Clasificación: 004.8 Inteligencia artificial. Razonamiento y aprendizaje automatizados. Sistemas inteligentes Resumen: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems Nota de contenido: Science of Information -- Part I Understanding Big Data -- Big Data Essentials -- Big Data Analytics -- Part II Understanding Big Data Systems -- Distributed File System -- MapReduce Programming Platform -- Part III Understanding Machine Learning -- Modeling and Algorithms -- Supervised Learning Models -- Supervised Learning Algorithms -- Support Vector Machine -- Decision Tree Learning -- Part IV Understanding Scaling-Up Machine Learning -- Random Forest Learning -- Deep Learning Models -- Chandelier Decision Tree -- Dimensionality Reduction En línea: http://dx.doi.org/10.1007/978-1-4899-7641-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=41468 Machine Learning Models and Algorithms for Big Data Classification : Thinking with Examples for Effective Learning [documento electrónico] / Suthaharan, Shan ; SpringerLink (Online service) . - Boston, MA : Springer US : Imprint: Springer, 2016 . - XIX, 359 p. 149 illus., 82 illus. in color : online resource. - (Integrated Series in Information Systems, ISSN 1571-0270; 36) .
ISBN : 978-1-4899-7641-3
Idioma : Inglés (eng)
Palabras clave: Business Management Database management Artificial intelligence and Intelligence (incl. Robotics) Clasificación: 004.8 Inteligencia artificial. Razonamiento y aprendizaje automatizados. Sistemas inteligentes Resumen: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems Nota de contenido: Science of Information -- Part I Understanding Big Data -- Big Data Essentials -- Big Data Analytics -- Part II Understanding Big Data Systems -- Distributed File System -- MapReduce Programming Platform -- Part III Understanding Machine Learning -- Modeling and Algorithms -- Supervised Learning Models -- Supervised Learning Algorithms -- Support Vector Machine -- Decision Tree Learning -- Part IV Understanding Scaling-Up Machine Learning -- Random Forest Learning -- Deep Learning Models -- Chandelier Decision Tree -- Dimensionality Reduction En línea: http://dx.doi.org/10.1007/978-1-4899-7641-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=41468 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Metagraphs and Their Applications Tipo de documento: documento electrónico Autores: Amit Basu ; SpringerLink (Online service) ; Blanning, Robert W Editorial: Boston, MA : Springer US Fecha de publicación: 2007 Colección: Integrated Series in Information Systems, ISSN 1571-0270 num. 15 Número de páginas: VIII, 172 p. 86 illus Il.: online resource ISBN/ISSN/DL: 978-0-387-37234-1 Idioma : Inglés (eng) Palabras clave: Mathematics Organization Planning Operations research Decision making Information technology Business Data processing Management information systems Computer science E-commerce Algebra IT in e-Commerce/e-business of Computing and Systems Operation Research/Decision Theory Clasificación: 51 Matemáticas Resumen: The graph is a critical and useful concept in designing many information processing systems. Systems such as transaction processing systems, decision support systems, and workflow systems are all helped immensely by a graphical structure. Simple graphs and digraphs allow for the construction of a variety of system design tools that provide a convenient and appealing format for illustrating information infrastructures, while allowing any subsequent analyses to be performed by the user. However, the metagraph, a new graphical structure that is developed in this book, goes beyond the representational and provides Information Systems with a robust, analytical modeling graphic tool. METAGRAPHS AND THEIR APPLICATIONS is a presentation of metagraph theory and its applications that begins by defining a metagraph and its uses. They are more complex than a simple graph structure, but they allow for representation and analysis of more complex systems. The material contained in this book is presented in two parts. The first develops the theoretical results with the emphasis on the development of a metagraph algebra. In the second part of the book, four promising applications of metagraphs are examined: 1) modeling of data relations, 2) the modeling of decision models, 3) the modeling of decision rules, and 4) the modeling of workflow tasks. Hence, the theoretical results in the initial chapters lay the foundation for the application areas in the second part of the book. The book concludes by examining several possible extensions of this work. Of special interest is the structuring of the metagraphs modeling process, which may enhance the body of work on systems analysis and design (including software engineering), the development of a metagraphs workbench to support such a process, and the possible application of the results presented here, suitably enhanced, to social networks Nota de contenido: Graphs, Hypergraphs, and Metagraphs -- Graphs, Hypergraphs, and Metagraphs -- Metagraph Theory -- The Algebraic Structure of Metagraphs -- Connectivity Properties of Metagraphs -- Metagraph Transformations -- Attributed Metagraphs -- Independent Sub-Metagraphs -- Applications of Metagraphs -- Metagraphs in Model Management -- Metagraphs in Data and Rule Management -- Metagraphs in Workflow and Process Analysis -- Conclusion En línea: http://dx.doi.org/10.1007/978-0-387-37234-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34458 Metagraphs and Their Applications [documento electrónico] / Amit Basu ; SpringerLink (Online service) ; Blanning, Robert W . - Boston, MA : Springer US, 2007 . - VIII, 172 p. 86 illus : online resource. - (Integrated Series in Information Systems, ISSN 1571-0270; 15) .
ISBN : 978-0-387-37234-1
Idioma : Inglés (eng)
Palabras clave: Mathematics Organization Planning Operations research Decision making Information technology Business Data processing Management information systems Computer science E-commerce Algebra IT in e-Commerce/e-business of Computing and Systems Operation Research/Decision Theory Clasificación: 51 Matemáticas Resumen: The graph is a critical and useful concept in designing many information processing systems. Systems such as transaction processing systems, decision support systems, and workflow systems are all helped immensely by a graphical structure. Simple graphs and digraphs allow for the construction of a variety of system design tools that provide a convenient and appealing format for illustrating information infrastructures, while allowing any subsequent analyses to be performed by the user. However, the metagraph, a new graphical structure that is developed in this book, goes beyond the representational and provides Information Systems with a robust, analytical modeling graphic tool. METAGRAPHS AND THEIR APPLICATIONS is a presentation of metagraph theory and its applications that begins by defining a metagraph and its uses. They are more complex than a simple graph structure, but they allow for representation and analysis of more complex systems. The material contained in this book is presented in two parts. The first develops the theoretical results with the emphasis on the development of a metagraph algebra. In the second part of the book, four promising applications of metagraphs are examined: 1) modeling of data relations, 2) the modeling of decision models, 3) the modeling of decision rules, and 4) the modeling of workflow tasks. Hence, the theoretical results in the initial chapters lay the foundation for the application areas in the second part of the book. The book concludes by examining several possible extensions of this work. Of special interest is the structuring of the metagraphs modeling process, which may enhance the body of work on systems analysis and design (including software engineering), the development of a metagraphs workbench to support such a process, and the possible application of the results presented here, suitably enhanced, to social networks Nota de contenido: Graphs, Hypergraphs, and Metagraphs -- Graphs, Hypergraphs, and Metagraphs -- Metagraph Theory -- The Algebraic Structure of Metagraphs -- Connectivity Properties of Metagraphs -- Metagraph Transformations -- Attributed Metagraphs -- Independent Sub-Metagraphs -- Applications of Metagraphs -- Metagraphs in Model Management -- Metagraphs in Data and Rule Management -- Metagraphs in Workflow and Process Analysis -- Conclusion En línea: http://dx.doi.org/10.1007/978-0-387-37234-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34458 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
