Описание: Object-Role Modeling (ORM) is a fact-based approach to data modelling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (eg: Person smokes), binary (eg: Person was born on Date), ternary (eg: Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modelled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORMs graphical notation. For the data modeller, ORMs graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualisation of the underlying semantics. Written as a sequel to the authors previous book "Object-Role Modeling Fundamentals", this book briefly reviews the fundamentals of ORM, and then discusses additional topics such as model reports generation, vocabulary glossaries, relational mapping options, annotated relational schemas, schema optimisation, and data modelling patterns. Written in easy-to-understand language, it illustrates each topic with simple examples, and explains how to use the freeware NORMA tool to implement the ideas discussed. The book also includes many practical exercises to promote expertise in the techniques covered, with answers provided to all the exercise questions.
Автор: Medeiros Kaelen Название: R Programming Fundamentals ISBN: 1789612993 ISBN-13(EAN): 9781789612998 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data analysis is crucial to accurately predict the performance of an application. The book begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you can read ahead and learn all about data visualization and ...
Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide
Key Features
Learn to create a digital prototype of a real model using hands-on examples
Evaluate the performance and output of your prototype using simulation modeling techniques
Understand various statistical and physical simulations to improve systems using Python
Book Description
Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.
Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.
By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
What you will learn
Gain an overview of the different types of simulation models
Get to grips with the concepts of randomness and data generation process
Understand how to work with discrete and continuous distributions
Work with Monte Carlo simulations to calculate a definite integral
Find out how to simulate random walks using Markov chains
Obtain robust estimates of confidence intervals and standard errors of population parameters
Discover how to use optimization methods in real-life applications
Run efficient simulations to analyze real-world systems
Who this book is for
Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
Автор: Halpin, Terry Название: Object-role modeling fundamentals ISBN: 1634620747 ISBN-13(EAN): 9781634620741 Издательство: Gazelle Book Services Рейтинг: Цена: 8792.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Object-Role Modeling (ORM) is a fact-based approach to data modelling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (eg: Person smokes), binary (eg: Person was born on Date), ternary (eg: Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORMs graphical notation. For the data modeler, ORMs graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualisation of the underlying semantics. Suitable for both novices and experienced practitioners, this book covers the fundamentals of the ORM approach. Written in easy-to-understand language, it shows how to design an ORM model, illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalisations of the model and map it to a relational database.
Описание: How do we design for data when traditional design techniques cannot extend to new database technologies? In this era of big data and the Internet of Things, it is essential that we have the tools we need to understand the data coming to us faster than ever before, and to design databases and data processing systems that can adapt easily to ever-changing data schemas and ever-changing business requirements. There must be no intellectual disconnect between data and the software that manages it. It must be possible to extract meaning and knowledge from data to drive artificial intelligence applications. Novel NoSQL data organization techniques must be used side-by-side with traditional SQL databases. Are existing data modeling techniques ready for all of this? The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases. This book will teach you: the simple and familiar graphical notation of COMN with its three basic shapes and four line styles; how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that arent tangled with confused techno-speak; how to express logical data designs that are freer from implementation considerations than is possible in any other notation; how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms; how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development. A quick reference guide to COMN is included in an appendix. The full notation reference is available at http://www.tewdur.com/
Автор: Miller Curtis Название: Training Systems using Python Statistical Modeling ISBN: 1838823735 ISBN-13(EAN): 9781838823733 Издательство: Неизвестно Рейтинг: Цена: 7171.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will acquaint you with various aspects of statistical analysis in Python. You will work with different types of prediction models, such as decision trees, random forests and neural networks. By the end of this book, you will be confident in using various Python packages to train your own models for effective machine learning.
Описание: Agile Data Warehouse Design is a step-by-step guide for capturing data warehousing/business intelligence (DW/BI) requirements and turning them into high performance dimensional models in the most direct way: by modelstorming (data modeling ] brainstorming) with BI stakeholders. This book describes BEAM✲, an agile approach to dimensional modeling, for improving communication between data warehouse designers, BI stakeholders and the whole DW/BI development team. BEAM✲ provides tools and techniques that will encourage DW/BI designers and developers to move away from their keyboards and entity relationship based tools and model interactively with their colleagues. The result is everyone thinks dimensionally from the outset Developers understand how to efficiently implement dimensional modeling solutions. Business stakeholders feel ownership of the data warehouse they have created, and can already imagine how they will use it to answer their business questions. Within this book, you will learn: ✲ Agile dimensional modeling using Business Event Analysis & Modeling (BEAM✲) ✲ Modelstorming: data modeling that is quicker, more inclusive, more productive, and frankly more fun ✲ Telling dimensional data stories using the 7Ws (who, what, when, where, how many, why and how) ✲ Modeling by example not abstraction; using data story themes, not crow's feet, to describe detail ✲ Storyboarding the data warehouse to discover conformed dimensions and plan iterative development ✲ Visual modeling: sketching timelines, charts and grids to model complex process measurement - simply ✲ Agile design documentation: enhancing star schemas with BEAM✲ dimensional shorthand notation ✲ Solving difficult DW/BI performance and usability problems with proven dimensional design patterns Lawrence Corr is a data warehouse designer and educator. As Principal of DecisionOne Consulting, he helps clients to review and simplify their data warehouse designs, and advises vendors on visual data modeling techniques. He regularly teaches agile dimensional modeling courses worldwide and has taught dimensional DW/BI skills to thousands of students. Jim Stagnitto is a data warehouse and master data management architect specializing in the healthcare, financial services, and information service industries. He is the founder of the data warehousing and data mining consulting firm Llumino.
Автор: Eric Nost, Jenny Goldstein Название: Nature of Data: Infrastructures, Environments, Politics ISBN: 149623250X ISBN-13(EAN): 9781496232502 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 3762.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise “revolutionary” advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges.
By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.
Автор: Eric Nost, Jenny Goldstein Название: Nature of Data: Infrastructures, Environments, Politics ISBN: 1496217152 ISBN-13(EAN): 9781496217158 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12415.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: When we look at some of the most pressing issues in environmental politics today, it is hard to avoid data technologies. Big data, artificial intelligence, and data dashboards all promise “revolutionary” advances in the speed and scale at which governments, corporations, conservationists, and even individuals can respond to environmental challenges.
By bringing together scholars from geography, anthropology, science and technology studies, and ecology, The Nature of Data explores how the digital realm is a significant site in which environmental politics are waged. This collection as a whole makes the argument that we cannot fully understand the current conjuncture in critical, global environmental politics without understanding the role of data platforms, devices, standards, and institutions. In particular, The Nature of Data addresses the contested practices of making and maintaining data infrastructure, the imaginaries produced by data infrastructures, the relations between state and civil society that data infrastructure reworks, and the conditions under which technology can further socio-ecological justice instead of re-entrenching state and capitalist power. This innovative volume presents some of the first research in this new but rapidly growing subfield that addresses the role of data infrastructures in critical environmental politics.
Автор: Roy Ishan Название: Blockchain Development for Finance Projects ISBN: 1838829091 ISBN-13(EAN): 9781838829094 Издательство: Неизвестно Рейтинг: Цена: 9010.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Blockchain technology will play a disruptive role in the banking and finance sector in the future. Experts estimate that it will save up to 20 billion dollars annually. You will build full-fledged financial applications using blockchain. It will aid you in building more secure and transparent workflows and re-engineering your business processes.
Автор: Zafar Iffat, Tzanidou Giounona, Burton Richard Название: Hands-on Convolutional Neural Networks with Tensorflow ISBN: 1789130336 ISBN-13(EAN): 9781789130331 Издательство: Неизвестно Рейтинг: Цена: 6068.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
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