17641Data compression
Chapter 1: Rationale and utilization; Chapter 2: Data compression techniques; Chapter 3: System considerations and data analysis; Chapter 4: Four software linkage considerations.
17642Data envelopment analysis : a comprehensive text with models, applications, references, and DEA-Solver software : 2nd ed.
General discussion. Basic CCR model. CCR model and production correspondence. Alternative dea models. Returns to scale. Models with restricted multipliers. Non-discretionary and categorical variables. Allocation models. Data variations. Super-efficiency models. Eficiency change over time . Scal elasticity and congestion. Undessirable outputs models. Economies ò scope and capacity utlilization. A dea game. Multi-stage use of parametric and non-parametric models.
17643Data management and file processing
Emphasizes the integration of data structures and file processing techniques for effective data management; presents a logical, easy-to-read blending of theoretical concepts and practical applications. Preserves the essence of rigorous mathematical treatment of the subject matter. Uses both ANS COBOL and pascal for presentation of algorithms. Includes thought-provoking end-of-chapter exercises and suggests programs and project activities to reinforce learning.
17644Data management at Scale : best practices for enterprise architecture
Chapter 1. The Disruption of Data Management. Chapter 2. Introducing the Scaled Architecture: Organizing Data at Scale. Chapter 3. Managing Vast Amounts of Data: The Read-Only Data. Chapter 4. Services and API Management: The API Architecture.
17645Data modeling
This book is about data modeling and ER diagrams. Chapter 1 discusses the role of data modeling in integrating organizational databases. Chapter 2 is that it can be used to develop object-oriented data models and semantic - oriented data models for object-oriented databases and semantic databases. Chapter 3 describes the optional-max approach for translating ER diagrams into relational tables. Chapter 4: A catalog of entity relationship diagrams. Chapter 5: Strategic datebase planning using a generic data model. Chapter 6: Object-Oriented .
17646Data modeling : a beginner's guide
Learn how to create data models that allow complex data to be analyzed, manipulated, extracted, and reported upon accurately. Data Modeling: A Beginner's Guide teaches you techniques for gathering business requirements and using them to produce conceptual, logical, and physical database designs. You'll get details on Unified Modeling Language (UML), normalization, incorporating business rules, handling temporal data, and analytical database design. The methods presented in this fast-paced tutorial are applicable to any database management system, regardless of vendor.
17647Data modeling for MongoDB : building well-designed and supportable MongoDB databases
This book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits.
17648Data modeling with NoSQL database : 3rd ed.
Data modeling has an important role to play in NoSQL environments. The data modeling process involves the creation of a diagram that represents the meaning of the data and the relationship between the data elements. Thus, understanding is a fundamental aspect of data modeling and a pattern for this kind of representation has few contributions for NoSQL databases. This edition (3rd) explains a NoSQL data modeling standard, introducing modeling techniques that can be used on document-oriented databases.
17649Data networks : 2nd ed.
Introduction and layered network architecture, Point-to-point protocols and links,Delay models in data networks, Multiaccess communication, Routing in data networks, Flow control.
17650Data science
Presenting to What is data science?. What is data and what is a dataset?. The data science ecosystem. Machine learning 101. Standard data science tasks. Privacy and ethics. Future trends and principles of success.