La finalidad de este libro es presentar los temas de minería de datos con un enfoque eminentemente práctico. El contenido de cada capítulo comienza exponiendo los conceptos adecuados, ilustrándolos a continuación con ejemplos prácticos resueltos con EnterÍndiceLa finalidad de este libro es presentar los temas de minería de datos con un enfoque eminentemente práctico. El contenido de cada capítulo comienza exponiendo los conceptos adecuados, ilustrándolos a continuación con ejemplos prácticos resueltos con Enterprise Miner, lo que constituye un valor añadido esencial de este texto. La interactividad de los ejemplos permite al lector ejecutarlos sobre la marcha basándose en los archivos contenidos en el CD-ROM que acompaña al libro.
¿Se ha preguntado alguna vez qué tienen en común una librería virtual, un servicio de venta de entradas por Internet y un banco electrónico? Todos estos sistemas hacen un uso intensivo de bases de datos y comparten un mismo tipo de interfaz gráfica de usuÍndiceEste libro pone al alcance de los lectores los elementos necesarios para diseñar arquitecturas de red de alto nivel con estándares de interconexión. Las técnicas explicadas le permitirán sacar el máximo partido a su base de datos, sea cual sea ésta, pues aunque los ejemplos efectúan conexiones con Oracle y Microsoft Access, el uso con otros sistemas gestores es inmediato. El texto tiene un marcado acento práctico, y se acompaña de multitud de ejemplos e ilustraciones, así como de un CD-ROM con muchas de las herramientas y servidores empleados.
La descripción de los comandos, funciones y el establecimiento de las diferencias existentes entre dbase III + hacen de esta guía un instrumento de gran ayuda para comparar las dos versiones y acoplar los trabajos realizados con anterioridad.
Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. This second edition includes numerous updates and new sections including an overview of ER/Studio’s support for agile development, as well as a description of some of ER/Studio’s newer features for NoSQL, such as MongoDB’s containment structure. You will build many ER/Studio data models along the way, applying best practices to master these ten objectives: Know why a data model is needed and which ER/Studio models are the most appropriate for each situation Understand each component on the data model and how to represent and create them in ER/Studio Know how to leverage ER/Studio’s latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases Know how to apply all the foundational features of ER/Studio Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design Improve data model quality and impact analysis results by leveraging ER/Studio’s lineage functionality and compare/merge utility Be able to apply ER/Studio’s data dictionary features Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats Leverage ER/Studio’s naming functionality to improve naming consistency, including the new Automatic Naming Translation feature. This book contains four sections: Section I introduces data modeling and the ER/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will learn about the newest features in ER/Studio (including features on big data and agile), and the ER/Studio environment. By the end of this section, you will have created and saved your first data model in ER/Studio and be ready to start modeling in Section II! Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to ‘read’ a data model of any size or complexity, and create a complete data model in ER/Studio. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio. Section IV discusses additional features of ER/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.
Este libro ofrece, de forma clara y concisa, tanto el modelo Entidad-Interrelación para la representación conceptual como los principios fundamentales en los que se basan las Bases de Datos Relacionales, además de las reglas que hay que seguir en el proceso de traducción de la representación conceptual del problema al esquema relacional. Cubre los conocimientos relacionados con las bases de datos, sistemas de gestión de bases de datos, modelos de datos conceptuales y relacionales, diseño conceptual de sistemas, diseño lógico relacional de bases de datos, álgebra relacional, y aplicación de los conocimientos con ejemplos prácticos haciendo uso de Oracle 7.3.
Espectáculo o entretenimiento, opinión e información pueden ser algunos de los términos que describan el papel que juega la televisión en nuestra sociedad. Este hecho ha motivado que no esté siendo desplazada por otros -algunos novedosos- medios de comunicación, sino que, al contrario de lo que se podría pensar, se está viendo potenciada por nuestra sociedad de la información, en la que se intenta mantener permanentemente informado al público para que este conozca la realidad. Para ello, la gestión de la documentación es vital tanto para la producción y realización de material audiovisual como para la contextualización y verificación de los datos necesarios para el desarrollo de la labor informativa. En el marco de dicha gestión documental, la información audiovisual está considerada piedra angular de dichos servicios informativos conforme a su propia naturaleza. Este trabajo pretende ser una herramienta que sirva de guía para el diseño e implementación de las bases de datos referenciales en el ámbito de las televisiones, independientemente de su temática y alcance geográfico, aportando pautas y normativas para la elaboración de registros en los que se analice la documentación audiovisual de televisión.
Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse — without them dozing off?Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives.Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours.Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book.As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general. This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT.
This is the seventh edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 10 Objectives 1. Explain data modeling components and identify them on your projects by following a question-driven approach 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3. Validate any data model with key “settings” (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard® 4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6. Practice finding structural soundness issues and standards violations 7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8. Use a series of templates for capturing and validating requirements, and for data profiling 9. Evaluate definitions for clarity, completeness, and correctness 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.
Data Modeling Made Simple with CA ERwin Data Modeler r8 will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You?ll build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. This book combines real-world experience and best practices with down to earth advice, humor, and even cartoons to help you master the following ten objectives: Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler Read a data model of any size and complexity with the same confidence as reading a book Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin?s Data Modelers Design Layer Architecture Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both ?top down? and bottom-up d
El libro muestra al usuario el funcionamiento y las posibilidades de este importante paquete con abundante ayuda de ejemplos y pantallas. La inclusión de una guía completa de comandos y funciones ordenadas según su aplicación, hace posible solucionar todo tipo de consultas.
Se asume que el lector ya tiene conocimientos previos de un lenguaje de modelado conceptual y de bases de datos relacionales. En este libro se aborda la problemática y la multiplicidad que supone obtener un esquema relacional a partir del esquema conceptual. Asimismo, se analizan las dificultades y las limitaciones de la implementación final de dichos esquemas sobre un sistema de gestión de bases de datos (SGBD) típico. Finalmente, se estudian las características de un buen diseño físico de bases de datos relacionales y, en particular, los parámetros de optimización y de ajuste (tuning) más habituales. En cuanto a los objetivos, se espera que el lector adquiera los conocimientos y las habilidades siguientes: transformar el esquema conceptual en unified modeling language (UML) a sentencias SQL de creación de tablas, expresando las diferentes claves y restricciones de integridad; distinguir entornos decisionales y operacionales; entender el plan de acceso de una consulta; optimizar las consultas críticas del sistema, y afinar (tuning) un SGBD.
If you found a rusty old lamp on the beach, and upon touching it a genie appeared and granted you three wishes, what would you wish for? If you were wishing for a successful application development effort, most likely you would wish for accurate and robust data models, comprehensive data flow diagrams, and an acute understanding of human behavior.The wish for well-designed conceptual and logical data models means the requirements are well-understood and that the design has been built with flexibility and extensibility leading to high application agility and low maintenance costs. The wish for detailed data flow diagrams means a concrete understanding of the business? value chain exists and is documented. The wish to understand how we think means excellent team dynamics while analyzing, designing, and building the application.Why search the beaches for genie lamps when instead you can read this book? Learn the skills required for modeling, value chain analysis, and team dynamics by following the journey the author and son go through in establishing a profitable summer lemonade business. This business grew fro
La finalidad de este libro es presentar los temas de minería de datos con un enfoque eminentemente práctico. El contenido de cada capítulo comienza exponiendo los conceptos adecuados, ilustrándolos a continuación con ejemplos prácticos resueltos con EnterÍndiceLa finalidad de este libro es presentar los temas de minería de datos con un enfoque eminentemente práctico. El contenido de cada capítulo comienza exponiendo los conceptos adecuados, ilustrándolos a continuación con ejemplos prácticos resueltos con Enterprise Miner, lo que constituye un valor añadido esencial de este texto. La interactividad de los ejemplos permite al lector ejecutarlos sobre la marcha basándose en los archivos contenidos en el CD-ROM que acompaña al libro. 
¿Se ha preguntado alguna vez qué tienen en común una librería virtual, un servicio de venta de entradas por Internet y un banco electrónico? Todos estos sistemas hacen un uso intensivo de bases de datos y comparten un mismo tipo de interfaz gráfica de usuÍndiceEste libro pone al alcance de los lectores los elementos necesarios para diseñar arquitecturas de red de alto nivel con estándares de interconexión. Las técnicas explicadas le permitirán sacar el máximo partido a su base de datos, sea cual sea ésta, pues aunque los ejemplos efectúan conexiones con Oracle y Microsoft Access, el uso con otros sistemas gestores es inmediato. El texto tiene un marcado acento práctico, y se acompaña de multitud de ejemplos e ilustraciones, así como de un CD-ROM con muchas de las herramientas y servidores empleados. 
La descripción de los comandos, funciones y el establecimiento de las diferencias existentes entre dbase III + hacen de esta guía un instrumento de gran ayuda para comparar las dos versiones y acoplar los trabajos realizados con anterioridad.
Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. This second edition includes numerous updates and new sections including an overview of ER/Studio’s support for agile development, as well as a description of some of ER/Studio’s newer features for NoSQL, such as MongoDB’s containment structure. You will build many ER/Studio data models along the way, applying best practices to master these ten objectives: Know why a data model is needed and which ER/Studio models are the most appropriate for each situation Understand each component on the data model and how to represent and create them in ER/Studio Know how to leverage ER/Studio’s latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases Know how to apply all the foundational features of ER/Studio Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design Improve data model quality and impact analysis results by leveraging ER/Studio’s lineage functionality and compare/merge utility Be able to apply ER/Studio’s data dictionary features Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats Leverage ER/Studio’s naming functionality to improve naming consistency, including the new Automatic Naming Translation feature. This book contains four sections: Section I introduces data modeling and the ER/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will learn about the newest features in ER/Studio (including features on big data and agile), and the ER/Studio environment. By the end of this section, you will have created and saved your first data model in ER/Studio and be ready to start modeling in Section II! Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to ‘read’ a data model of any size or complexity, and create a complete data model in ER/Studio. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio. Section IV discusses additional features of ER/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.
Este libro ofrece, de forma clara y concisa, tanto el modelo Entidad-Interrelación para la representación conceptual como los principios fundamentales en los que se basan las Bases de Datos Relacionales, además de las reglas que hay que seguir en el proceso de traducción de la representación conceptual del problema al esquema relacional. Cubre los conocimientos relacionados con las bases de datos, sistemas de gestión de bases de datos, modelos de datos conceptuales y relacionales, diseño conceptual de sistemas, diseño lógico relacional de bases de datos, álgebra relacional, y aplicación de los conocimientos con ejemplos prácticos haciendo uso de Oracle 7.3.
Espectáculo o entretenimiento, opinión e información pueden ser algunos de los términos que describan el papel que juega la televisión en nuestra sociedad. Este hecho ha motivado que no esté siendo desplazada por otros -algunos novedosos- medios de comunicación, sino que, al contrario de lo que se podría pensar, se está viendo potenciada por nuestra sociedad de la información, en la que se intenta mantener permanentemente informado al público para que este conozca la realidad. Para ello, la gestión de la documentación es vital tanto para la producción y realización de material audiovisual como para la contextualización y verificación de los datos necesarios para el desarrollo de la labor informativa. En el marco de dicha gestión documental, la información audiovisual está considerada piedra angular de dichos servicios informativos conforme a su propia naturaleza. Este trabajo pretende ser una herramienta que sirva de guía para el diseño e implementación de las bases de datos referenciales en el ámbito de las televisiones, independientemente de su temática y alcance geográfico, aportando pautas y normativas para la elaboración de registros en los que se analice la documentación audiovisual de televisión.
Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse — without them dozing off?Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives.Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours.Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book.As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general. This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT.
This is the seventh edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard®. You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 10 Objectives 1. Explain data modeling components and identify them on your projects by following a question-driven approach 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3. Validate any data model with key “settings” (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard® 4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6. Practice finding structural soundness issues and standards violations 7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8. Use a series of templates for capturing and validating requirements, and for data profiling 9. Evaluate definitions for clarity, completeness, and correctness 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture. 
Data Modeling Made Simple with CA ERwin Data Modeler r8 will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You?ll build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. This book combines real-world experience and best practices with down to earth advice, humor, and even cartoons to help you master the following ten objectives: Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler Read a data model of any size and complexity with the same confidence as reading a book Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin?s Data Modelers Design Layer Architecture Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both ?top down? and bottom-up d
El libro muestra al usuario el funcionamiento y las posibilidades de este importante paquete con abundante ayuda de ejemplos y pantallas. La inclusión de una guía completa de comandos y funciones ordenadas según su aplicación, hace posible solucionar todo tipo de consultas.
Se asume que el lector ya tiene conocimientos previos de un lenguaje de modelado conceptual y de bases de datos relacionales. En este libro se aborda la problemática y la multiplicidad que supone obtener un esquema relacional a partir del esquema conceptual. Asimismo, se analizan las dificultades y las limitaciones de la implementación final de dichos esquemas sobre un sistema de gestión de bases de datos (SGBD) típico. Finalmente, se estudian las características de un buen diseño físico de bases de datos relacionales y, en particular, los parámetros de optimización y de ajuste (tuning) más habituales. En cuanto a los objetivos, se espera que el lector adquiera los conocimientos y las habilidades siguientes: transformar el esquema conceptual en unified modeling language (UML) a sentencias SQL de creación de tablas, expresando las diferentes claves y restricciones de integridad; distinguir entornos decisionales y operacionales; entender el plan de acceso de una consulta; optimizar las consultas críticas del sistema, y afinar (tuning) un SGBD.
If you found a rusty old lamp on the beach, and upon touching it a genie appeared and granted you three wishes, what would you wish for? If you were wishing for a successful application development effort, most likely you would wish for accurate and robust data models, comprehensive data flow diagrams, and an acute understanding of human behavior.The wish for well-designed conceptual and logical data models means the requirements are well-understood and that the design has been built with flexibility and extensibility leading to high application agility and low maintenance costs. The wish for detailed data flow diagrams means a concrete understanding of the business? value chain exists and is documented. The wish to understand how we think means excellent team dynamics while analyzing, designing, and building the application.Why search the beaches for genie lamps when instead you can read this book? Learn the skills required for modeling, value chain analysis, and team dynamics by following the journey the author and son go through in establishing a profitable summer lemonade business. This business grew fro