Databases / Data management Books

Book SynopsisThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings. Table of ContentsIntroduction to Data Analytics (DA). Why Data Analytics in Project Management (PM)? The Importance of DA in PM. The Key role of Data Analytics in Business Analysis. Business Analysis in Managing Projects. Earned Value Method. IT solutions of DA as Applied to PM. How to manage Big Data issues in Projects’ Environment. Data Mining and the Project Management Office. Project Portfolio Management. Future Directions.

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Book SynopsisTaking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories. This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world. Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism's heavy lifting searching for, scraping and cleaning data. Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis. Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructorTable of ContentsIntroductionChapter 1: Acquiring DataChapter 2: Searching the Deep WebChapter 3: Scraping DataChapter 4: Cleaning DataChapter 5: Basic SpreadsheetsChapter 6: Advanced Spreadsheets and RChapter 7: Writing a Data StoryChapter 8: SQLChapter 9: Scraping Social MediaChapter 10: Data VisualizationChapter 11: Ethics, Trust, Transparency and Posting Data OnlineChapter 12: Math for Journalists: Writing with Numbers

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Book SynopsisUrban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools.The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them. Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others.Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal. Table of Contents1 Introduction 2 Welcome to R 3 Telling a Data Story: Examining Individual Records 4 The Pulse of the City: Observing Variable Patterns 5 Uncovering Information: Making and Creating Variables 6 Measuring with Big Data 7 Making Measures from Records: Aggregating and Merging Data 8 Mapping Communities 9 Advanced Visual Techniques 10 Beyond Measurement: Inferential Statistics (and Correlations) 11 Identifying Inequities across Groups: ANOVA and t-Test 12 Unpacking Mechanisms Driving Inequities: Multivariate Regression 13 Advanced Analytic Techniques 14 Emergent Technologies

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Book SynopsisPulling aside the curtain of Big Data' buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals how data is generated, organized, stored, evaluated, and maintained has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as wTable of Contents1 Preface. 2 Data – Introduction. 3 The Many Facets of Data. 3.1 Basic Concepts. 3.2 Basic Terms and Terminology. 4 Domain Specific Topics. 4.1 Data Governance. 4.2 Data Architecture. 4.3 Databases. 4.4 Master Data and Master Data Management. 4.5 Metadata and Metadata Management. 4.6 Data Quality. 4.7 Null Values. 4.8 Data Modeling and Design. 4.9 Data Integration and Interoperability. 4.10 Data Security. 4.11 Data at Rest and Data in Motion. 4.12 Data Wrangling and Data Storage. 5 Data: Past, Present and Future. 5.1 Data – The Past. 5.2 Data – The Present. 5.3 Data – The Future. 6 The New Reality. 7 Data – Use Cases.8 To Sum Up. 9 Data – Optimization. 10 Epilog

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Book SynopsisThis book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and tidy them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatialTable of Contents1. Data Types 2. Data Transformation 3. Data Visualization 4. Building Simulations 5. Sampling 6. Hypothesis Testing 7. Quantifying Uncertainty 8. Towards Normality 9. Regression 10. Text Analysis

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Book SynopsisThere currently is no in-depth book dedicated to the challenge of the Internet of Everything and Big Data technologies in smart cities. Humankind today is confronting a critical worldwide portability challenge and the framework that moves cities must keep pace with the innovation. Internet of Everything and Big Data: Major Challenges in Smart Cities reviews the applications, technologies, standards, and other issues related to smart cities.This book is dedicated to addressing the major challenges in realizing smart cities and sensing platforms in the era of Big Data cities and Internet of Everything. Challenges vary from cost and energy efficiency to availability and service quality. This book examines security issues and challenges, addresses the total information science challenges, covers exploring and creating IoT environment-related sales adaptive systems, and investigates basic and high-level concepts using the latest techniques implemented by researchers Table of Contents1. Wireless Sensor Networks in Smart Cities. 2. Big Data Analytics. 3. Security Issues in Smart Cities. 4. Artificial Intelligence in Smart-Cities. 5. Performability in IoT-enabled Sensors. 6. Data delivery in IoT-enabled Smart Cities. 7. Deployment Issues in IoT-enabled Sensors. 8. Traffic Modelling in Smart-Cities. 9. Resource Management and Enabling Technologies Localization in IoT-enabled Sensors. 10. Modeling and Simulation with Fuzzy Techniques in Smart Cities. 11. Energy Efficiency in Smart Cities Technologies. 12. Semantic Interoperability for IoT.

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Book SynopsisThe 2022 World Economic Forum surveyed 1,000 experts and leaders who indicated their risk perception that the earth's conditions for humans are a main concern in the next 10 years. This means environmental risks are a priority to study in a formal way. At the same time, innovation risks are present in theminds of leaders, newknowledge brings new risk, and the adaptation and adoption of risk knowledge is required to better understand the causes and effects can have on technological risks. These opportunities require not only adopting new ways of managing and controlling emerging processes for society and business, but also adapting organizations to changes and managing new risks.Risk Analytics: Data-Driven Decisions Under Uncertainty introduces a way to analyze and design a risk analytics system (RAS) that integrates multiple approaches to risk analytics to deal with diverse types of data and problems. A risk analytics system is a hybrid system where human andTable of Contents1. Fundamental Concepts 2. Risk Management, Modelling, and Analytics Processes 3. Decision Making under Risk and Its Analytics Support 4. Risk Management and Analytics in Organizations 5. Tools for Risk Management 6. Data Analytics in Risk Management 7. Machine and Statistical Learning in Risk Analytics 8. Dealing with Monitoring the Risk Analytics Process 9. Creation of Actions and Value

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Book SynopsisWhat if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities.This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods.Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real expertsâthose living and working within our communities.Discover more at: www.opendataforeverybody.com

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Book SynopsisBig data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done.A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methoTrade Review'This book makes applied econometric methods accessible to anyone interested in quantitative economic history' — Helen Paul, University of Southampton, UK.Table of Contents1. Introduction: Research methods for large databases Mark Casson and Nigar Hashimzade 2. Long-run Price Dynamics: The measurement of substitutability between commodities Mark Casson, Nigar Hashimzade and Catherine Casson 3. The Quantity Theory of Money in Historical Perspective Nick Mayhew 4. Medieval Foreign Exchange: A time series analysis Adrian Bell, Chris Brooks and Tony K. Moore 5. Local Property Values in Fourteenth and Fifteenth-century England Margaret Yates, Anna Campbell and Mark Casson 6. Visual Analytics for Large-scale Actor Networks, with an Application to Liverpool Business Networks John Haggerty and Sheryllynne Haggerty 7. Railways and Local Population Growth: Northamptonshire and Rutland, 1801-91 Mark Casson, Leigh Shaw-Taylor, A.E.M. Satchell and E.A. Wrigley 8. Women’s Land Ownership in Nineteenth-century England Janet Casson 9. The Diffusion of Steam Technology in England: Ploughing engines, 1860-1930 Jane McCutchan 10. Industrious Burglars: Funding consumption from property crime Jane Humphries, Sara Horrell and Ken Sneath

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Book SynopsisThis book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issueTable of Contents1 Large-Scale Video Event Detection Using Deep Neural Networks 2 Leveraging Selectional Preferences for Anomaly Detection in Newswire Events 3 Abnormal Event Recognition in Crowd Environments 4 Cognitive Sensing: Adaptive Anomalies Detection with Deep Networks 5 Language-Guided Visual Recognition 6 Deep Learning for Font Recognition and Retrieval 7 A Distributed Secure Machine-Learning Cloud Architecture for Semantic Analysis 8 A Practical Look at Anomaly Detection Using Autoencoders with H2O and the R Programming Language

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Book SynopsisThe development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradiTable of ContentsPrefaceEditorsContributors1 Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition AssessmentAmir H. Alavi and William G. Buttlar2 Global Satellite Observations for Smart CitiesZhong Liu, Menglin S. Jin, Jacqueline Liu, Angela Li, William Teng, Bruce Vollmer, and David Meyer3 Advancing Smart and Resilient Cities with Big Spatial Disaster Data: Challenges, Progress, and OpportunitiesXuan Hu and Jie Gong4 Smart City Portrayal: Dynamic Visualization Applied to the Analysis of Underground MetroEvgheni Polisciuc and Penousal Machado5 Smart Bike-Sharing Systems for Smart CitiesHesham A. Rakha, Mohammed Elhenawy, Huthaifa I. Ashqar, Mohammed H. Almannaa, and Ahmed Ghanem6 Indirect Monitoring of Critical Transport Infrastructure: Data Analytics and Signal ProcessingAbdollah Malekjafarian, Eugene J. OBrien, and Fatemeh Golpayegani7 Big Data Exploration to Examine Aggressive Driving Behavior in the Era of Smart CitiesArash Jahangiri, Sahar Ghanipoor Machiani, and Vahid Balali8 Exploratory Analysis of Run-Off-Road Crash PatternsMohammad Jalayer, Huaguo Zhou, and Subasish Das9 Predicting Traffic Safety Risk Factors Using an Ensemble ClassifierNasim Arbabzadeh, Mohammad Jalayer, and Mohsen Jafari10 Architecture Design of Internet of Things-Enabled Cloud Platform for Managing the Production of Prefabricated Public HousesClyde Zhengdao Li, Bo Yu, Cheng Fan, and Jingke HongIndex.

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Book SynopsisData Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today's information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more.In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, anTrade Review"In the tradition of Edward Tufte’s design strategies for visual and narrative representations that frame our shared reality, Sosulski has artfully crafted a functional guide to design patterns and processes for shaping expressions of data and, most importantly, its usage in real-world organizational contexts. This will be a go-to reference in my library for years to come."—Jason Severs, Chief Design Officer, Droga5Table of Contents1. Becoming Visual 2. The Tools 3. The Graphics 4. The Data 5. The Design 6. The Audience 7. The Presentation 8. The Cases 9. The End

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Book SynopsisThere is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.Table of ContentsBig Data Applications in Lung Research. Artificial convolution neural network techniques and applications for big data of lung for nodule detection. Deep learning with non-medical training used for pathology identification in big data chest images. Unsupervised pre-training across image domains improves lung tissue classification in lung big data sets. Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks in big data sets of CT Lungs. Big Data Applications in Colon Research. A comprehensive computer-aided polyp detection system for big data colonoscopy videos. Automatic polyp detection in big data colonoscopy videos using an ensemble of convolutional neural networks. A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations in big data colonoscopy. Off-the-shelf convolutional neural network features for pulmonary nodule detection in big data computed tomography scans. Big Data Applications in Breast Cancer. Mitosis detection in big data breast cancer histology images with deep neural networks. Convolutional neural networks for mass lesion classification in big data mammography. Standard plane localization in fetal ultrasound via domain transferred deep neural networks in large ultrasound data sets. Unregistered multiview analysis with pre-trained deep learning models in large mammographic data sets. Big Data Applications in Brain Imaging. Brain tumor segmentation with deep neural networks using big data sets. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation in big data MRI images. Deep neural networks segment neuronal membranes in electron microscopy images. Alzheimer's Disease Diagnosis by Adaptation of 3D Convolutional Network in large MRI brain images. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Big Data Applications in Heart Imaging. Automating carotid intima-media thickness video interpretation with convolutional neural networks. Interleaved text/image deep mining on a very large-scale radiology database. Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition in big data sets. Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks in large MRI populations. Big Data Applications in Urology and Abdomen Imaging. A New NMF-Autoencoder Based CAD System for Early Diagnosis of Prostate Cancer by considering big data sets. Image-Based Computer-Aided Diagnosis for Early Diagnosis of Prostate Cancer in large data sets. Deep convolutional networks for pancreas segmentation in large scale CT imaging. A Promising Non-invasive CAD System for Kidney Function Assessment.

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Book SynopsisFully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed “where it belongs”. The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server.The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, OracTable of Contents1. Overview of Databases 2. Normalization 3. Database Implementation 4. Normalization and Physical Design Exercise 5. The erwin Data Modeling Tool 6. Using Microsoft Access 7. Using SQL Server 8. Using Perl to Extract and Load Data 9. Building User Interfaces 10. Creating the University Database Application 11. PHP Implementation and Used

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Book SynopsisIn order to make informed decisions, there are three important elements: intuition, trust, and analytics. Intuition is based on experiential learning and recent research has shown that those who rely on their gut feelings may do better than those who don't. Analytics, however, are important in a data-driven environment to also inform decision making. The third element, trust, is critical for knowledge sharing to take place. These three elementsintuition, analytics, and trustmake a perfect combination for decision making. This book gathers leading researchers who explore the role of these three elements in the process of decision-making.Table of ContentsIntuition. The Underpinnings of Intuition. How Intuition Affects Decision Making. Data, Insights, Models, and Decisions. The Missing Link—Experiential Learning. Cases of Intuition Outperforming Analytics. Trust. The Foundation of Trust. Trust and Organizational Leadership. Trust and Knowledge Sharing. Trust and Organizational Communication. Trust and Marketing. Trust and Social Media. Analytics. The Secret Sauce. Predictive Analytics. Prescriptive Analytics. Developing an Analytics Strategy. Looking Toward the Future with Cognitive Computing and AI.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThe last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.Table of Contents1. Key developments in algorithmic randomness Johanna N. Y. Franklin and Christopher P. Porter; 2. Algorithmic randomness in ergodic theory Henry Towsner; 3. Algorithmic randomness and constructive/computable measure theory Jason Rute; 4. Algorithmic randomness and layerwise computability Mathieu Hoyrup; 5. Relativization in randomness Johanna N. Y. Franklin; 6. Aspects of Chaitin's Omega George Barmpalias; 7. Biased algorithmic randomness Christopher P. Porter; 8. Higher randomness Benoit Monin; 9. Resource bounded randomness and its applications Donald M. Stull; Index.

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Book SynopsisDavid M. Kroenke entered  the computing profession as a summer intern at the RAND Corporation  in 1967. Since then, his career has spanned  education,  industry, consulting, and publishing. He has taught at the University of Washington, Colorado State University, and Seattle University. Over the years, he has led dozens of teaching seminars for college professors. In 1991 the International Association of Information Systems named him Computer Educator of the Year. In  industry,  Kroenke  has  worked  for  the  U.S. Air  Force  and  Boeing Computer Services, and he was a principal in the startup of three companies. He was also vice presi- dent of product  marketing and development for the Microrim Corporation  and was chief technologist for the database division of Wall Data, Inc. He is the father of the semantic object data mTable of Contents PAR T 1 DATABASE FUNDAMENTALS 1 Getting Started 2 The Relational Model 3 Structured Query Language PAR T 2 DATABASE DESIGN 4 Data Modeling and the Entity- Relationship Model 5 Database Design PAR T 3 DATABASE MANAGEMENT 6 Database Administration 7 Database Processes Applications 8 Data Warehouses, Business Intelligence Systems, and Big Data Glossary Index

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Book SynopsisUnderstanding Databases: Concepts and Practice is an accessible, highly visual introduction to database systems for undergraduate students across many majors. Designed for self-contained first courses in the subject, this interactive e-textbook covers fundamental database topics including conceptual design, the relational data model, relational algebra and calculus, Structured Query Language (SQL), database manipulation, transaction management, and database design theory. Visual components and self-assessment features provide a more engaging and immersive method of learning that enables students to develop a solid foundation in both database theory and practical application. Concise, easy-to-digest chapters offer ample opportunities for students to practice and master the material, and include a variety of solved real-world problems, self-check questions, and hands-on collaborative activities that task students to build a functioning database. This Enhanced eText also Table of ContentsPreface xi 1 Introduction to Databases and the Relational Data Model 1 1.1 Databases are a Tool 2 1.2 Overview of Data and Models 6 1.3 The Relational Data Model 14 1.3.1 Definition 14 1.3.2 Uniqueness 16 1.3.3 Referential Integrity 20 Special Topic: Primary Key and Referential Integrity 26 1.3.4 Additional Constraints 27 2 Conceptual Design 43 2.1 Gathering Requirements 44 2.2 Entity-Relationship Diagrams 46 How To: Design an Entity-Relationship Diagram 50 Special Topic: (Min, Max) Pairs 54 Special Topic: Recursive Relationships and Role Names 54 Special Topic: Ternary Relationships 56 Special Topic: EER for Modeling Inheritance 56 2.3 Mapping ER Diagrams to Tables 62 How To: Map an ER Diagram to Relations 67 2.4 Other Graphical Approaches 73 3 Relational Algebra 103 3.1 Query Design 104 How To: Query Design 104 3.2 Algebra Operators 109 Note: Overview of Relational Algebra 110 3.2.1 Filtering 111 3.2.2 Sets 114 3.2.3 Joins 116 3.2.4 Division 119 3.3 Relational Completeness 127 3.4 Query Optimization 134 How To: Heuristic Query Optimization 136 4 Relational Calculus 161 4.1 Logical Foundations 162 Note: Overview of Relational Calculus Languages 163 4.2 Tuple Relational Calculus 164 4.2.1 Fundamental Query Expressions 165 How To: Writing a Fundamental Query in TRC 165 vi 4.2.2 Quantification of Variables 167 4.2.3 Atoms and Formula 172 4.2.4 Relational Completeness 175 4.3 Domain Relational Calculus 183 4.3.1 Fundamental Query Expressions 183 How To: Writing a Fundamental Query in DRC 184 4.3.2 Quantification of Variables 187 4.3.3 Atoms and Formula 193 4.3.4 Relational Completeness 195 4.4 Safety 204 5 SQL: An Introduction to Querying 237 5.1 Foundations 237 Note: SQL Syntax 238 Syntax: Basic SQL Query 240 5.2 Fundamental Query Expressions 246 How To: Writing a Fundamental Query in SQL 246 5.2.1 Queries involving One Table 247 5.2.2 Queries involving Multiple Tables 250 How To: Writing a Reflection Query 255 5.3 Nested Queries 261 Special Topic: A glimpse at query optimization 264 Special Topic: Views and Inline Views 266 5.4 Set Operators 270 5.5 Aggregation and Grouping 276 Special Topic: Arithmetic Expressions 281 5.6 Querying with null Values 285 5.7 Relational Completeness 289 5.7.1 Fundamental Operators 290 5.7.2 Additional Operators 292 6 SQL: Beyond the Query Language 329 6.1 Data Definition 329 Syntax: Create Table Statement 331 Syntax: Drop Table Statement 336 Syntax: Alter Table Statement 337 Special Topic: Create Index 338 Syntax: Create View Statement 339 6.2 Data Manipulation 342 Syntax: Insert Into Statement 343 Special Topic: Database Population 346 Syntax: Update Statement 347 Syntax: Delete Statement 349 6.3 Database User Privileges 352 Syntax: Grant Statement 354 Syntax: Revoke Statement 355 7 Database Programming 371 7.1 Persistent Stored Modules 372 Syntax: Create Procedure Statement 374 Syntax: Create Function Statement 376 7.2 Overview of Call-Level Interface 382 7.3 Java and JDBC 385 7.4 Python and DB-API 393 8 XML and Databases 431 8.1 Overview of XML 432 8.2 DTD 439 Syntax: DTD Overview 440 8.3 XML Schema 448 Syntax: XSD Overview of Element and Attribute Declarations 450 Syntax: XSD Attribute Declarations: use, default, fixed 460 8.4 Structuring XML for Data Exchange 467 9 Transaction Management 491 9.1 ACID Properties of a Transaction 492 9.2 Recovery control 498 How To: Recovery Control: UNDO and REDO 501 9.3 Concurrency control 504 9.3.1 Serializability 507 How To: Create a Precedence Graph 508 9.3.2 Locking 512 9.3.3 Timestamps 520 Algorithm: Basic Timestamp Protocol 521 10 More on Database Design 543 10.1 Database Design Goals 544 10.2 Functional Dependencies 546 Algorithm: Attribute Closure 550 Special Topic: Minimal Set of Functional Dependencies 552 How To: Heuristic Determination of a Candidate Key 552 10.3 Decomposition 558 How To: Determine Breakdown of F for a Decomposition 559 How To: Determine Lossless Pairwise Decomposition 562 Algorithm: Lossless-Join Property for Database Schema 565 10.4 Normal Forms 571 How To: Determine the Normal Form of a Relation 574 Algorithm: BCNF Decomposition Algorithm 575 A WinRDBI 599 A.1 Overview 599 A.2 Query Languages 600 A.3 Implementation Overview 606 A.4 Summary 606 Index 607

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Book SynopsisTable of ContentsIntroduction xv Assessment Test xxii Chapter 1 Today’s Data Analyst 1 Welcome to the World of Analytics 2 Data 2 Storage 3 Computing Power 4 Careers in Analytics 5 The Analytics Process 6 Data Acquisition 7 Cleaning and Manipulation 7 Analysis 8 Visualization 8 Reporting and Communication 8 Analytics Techniques 10 Descriptive Analytics 10 Predictive Analytics 11 Prescriptive Analytics 11 Machine Learning, Artificial Intelligence, and Deep Learning 11 Data Governance 13 Analytics Tools 13 Summary 15 Chapter 2 Understanding Data 17 Exploring Data Types 18 Structured Data Types 20 Unstructured Data Types 31 Categories of Data 36 Common Data Structures 39 Structured Data 39 Unstructured Data 41 Semi-structured Data 42 Common File Formats 42 Text Files 42 JavaScript Object Notation 44 Extensible Markup Language (XML) 45 HyperText Markup Language (HTML) 47 Summary 48 Exam Essentials 49 Review Questions 51 Chapter 3 Databases and Data Acquisition 57 Exploring Databases 58 The Relational Model 59 Relational Databases 62 Nonrelational Databases 68 Database Use Cases 71 Online Transactional Processing 71 Online Analytical Processing 74 Schema Concepts 75 Data Acquisition Concepts 81 Integration 81 Data Collection Methods 83 Working with Data 88 Data Manipulation 89 Query Optimization 96 Summary 99 Exam Essentials 100 Review Questions 101 Chapter 4 Data Quality 105 Data Quality Challenges 106 Duplicate Data 106 Redundant Data 107 Missing Values 110 Invalid Data 111 Nonparametric data 112 Data Outliers 113 Specification Mismatch 114 Data Type Validation 114 Data Manipulation Techniques 116 Recoding Data 116 Derived Variables 117 Data Merge 118 Data Blending 119 Concatenation 121 Data Append 121 Imputation 122 Reduction 124 Aggregation 126 Transposition 127 Normalization 128 Parsing/String Manipulation 130 Managing Data Quality 132 Circumstances to Check for Quality 132 Automated Validation 136 Data Quality Dimensions 136 Data Quality Rules and Metrics 140 Methods to Validate Quality 142 Summary 144 Exam Essentials 145 Review Questions 146 Chapter 5 Data Analysis and Statistics 151 Fundamentals of Statistics 152 Descriptive Statistics 155 Measures of Frequency 155 Measures of Central Tendency 160 Measures of Dispersion 164 Measures of Position 173 Inferential Statistics 175 Confidence Intervals 175 Hypothesis Testing 179 Simple Linear Regression 186 Analysis Techniques 190 Determine Type of Analysis 190 Types of Analysis 191 Exploratory Data Analysis 192 Summary 192 Exam Essentials 194 Review Questions 196 Chapter 6 Data Analytics Tools 201 Spreadsheets 202 Microsoft Excel 203 Programming Languages 205 R 205 Python 206 Structured Query Language (SQL) 208 Statistics Packages 209 IBM SPSS 210 SAS 211 Stata 211 Minitab 212 Machine Learning 212 IBM SPSS Modeler 213 RapidMiner 214 Analytics Suites 217 IBM Cognos 217 Power BI 218 MicroStrategy 219 Domo 220 Datorama 221 AWS QuickSight 222 Tableau 222 Qlik 224 BusinessObjects 225 Summary 225 Exam Essentials 225 Review Questions 227 Chapter 7 Data Visualization with Reports and Dashboards 231 Understanding Business Requirements 232 Understanding Report Design Elements 235 Report Cover Page 236 Executive Summary 237 Design Elements 239 Documentation Elements 244 Understanding Dashboard Development Methods 247 Consumer Types 247 Data Source Considerations 248 Data Type Considerations 249 Development Process 250 Delivery Considerations 250 Operational Considerations 252 Exploring Visualization Types 252 Charts 252 Maps 258 Waterfall 264 Infographic 266 Word Cloud 267 Comparing Report Types 268 Static and Dynamic 268 Ad Hoc 269 Self-Service (On-Demand) 269 Recurring Reports 269 Tactical and Research 270 Summary 271 Exam Essentials 272 Review Questions 274 Chapter 8 Data Governance 279 Data Governance Concepts 280 Data Governance Roles 281 Access Requirements 281 Security Requirements 286 Storage Environment Requirements 289 Use Requirements 291 Entity Relationship Requirements 292 Data Classification Requirements 292 Jurisdiction Requirements 297 Breach Reporting Requirements 298 Understanding Master Data Management 299 Processes 300 Circumstances 301 Summary 303 Exam Essentials 304 Review Questions 306 Appendix Answers to the Review Questions 311 Chapter 2: Understanding Data 312 Chapter 3: Databases and Data Acquisition 314 Chapter 4: Data Quality 315 Chapter 5: Data Analysis and Statistics 317 Chapter 6: Data Analytics Tools 319 Chapter 7: Data Visualization with Reports and Dashboards 322 Chapter 8: Data Governance 323 Index 327

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Book SynopsisDelivering concise, cutting-edge coverage, CONCEPTS OF DATABASE MANAGEMENT, 8e uses real-world cases, examples, and illustrations to give students a thorough understanding of such critical issues as database design, data integrity, concurrent updates, data security, and more. Completely updated to Microsoft Access 2013 standards, the text presents SQL in a database-neutral environment and covers all major topics, including E-R diagrams, normalization, and database design. It provides detailed coverage of the relational model (including QBE and SQL), normalization and views, database administration and management, and more. Advanced topics covered include distributed databases, data warehouses, stored procedures, triggers, data macros, and Web Apps. Ideal for an introductory database course in an information systems, business, or CIS program, CONCEPTS OF DATABASE MANAGEMENT, 8e can be used in varying disciplines by instructors who want database coverage without using a trade book or a lTable of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: How Do I" Reference. Appendix D: Answers to Odd-Numbered Review Questions. Appendix E: Access Web Apps. Appendix F: Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisGain a thorough, applied understanding of critical database issues with Starks/Pratt/Last's CONCEPTS OF DATABASE MANAGEMENT, 9E. Real cases, examples and screenshots in this concise presentation help clarify database design, data integrity, normalization, concurrent updates, data security, and big data. Completely updated to SQL Server 2016, Microsoft Access 2016, and Office 365 standards, this edition explores SQL in a database-neutral environment while addressing E-R diagrams, normalization, and database design. Detailed coverage presents the relational model (including QBE and SQL), normalization and views, database administration and management. You also examine advanced topics such as distributed databases, data warehouses, stored procedures, triggers, data macros and Web Apps. This introduction to database is ideal for mastering today's database techniques.Table of Contents1. Introduction to Database Management. 2. The Relational Model 1: Introduction, QBE, and Relational Algebra. 3. The Relational Model 2: SQL. 4. The Relational Model 3: Advanced Topics. 5. Database Design 1: Normalization. 6. Database Design 2: Design Method. 7. DBMS Functions. 8. Database Administration. 9. Database Management Approaches. Appendix A: Comprehensive Design Example: Marvel College. Appendix B: SQL Reference. Appendix C: MySQL. Appendix D: How Do I" Reference. Appendix E: Using Access to Create a Web App. Appendix F: A Systems Analysis Approach to Information-Level Requirements."

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Book SynopsisIn order to become an IBM Certified Database Administrator - DB2 9 DBA for z/OS, you must pass two exams: DB2 9 Fundamentals Exam (Exam 730), and DB2 9 Database Administrator for z/OS (Exam 732)—the primary focus focus of this book.Written by two members of the team who participated in the actual writing of the exam, this specialized study guide covers every topic that you will need to know to pass Exam 732, including database design and implementation, operation and recovery, security and auditing, performance, as well as installation and migration/upgrade. But that is only the beginning. It also covers the new features of DB2 9 for both database and application development.This comprehensive guide includes an extensive set of practice questions in each chapter that closely model the actual exam, along with an answer key with a description of why the answer is the correct one. No other source gives you this much help in passing the exam.Whether you plan to take Exam 732 or just want to master the skills needed to be an effective database administrator on z/OS systems, this is the only book you’ll need.With the DB2 9 for z/OS Database Administration Certification Study Guide, you will:• Discover the changes to DB2 9 that you’ll need to know in order to be successful when taking the exam • Learn how to effectively administer a DB2 database • Receive an explanation of every objective included on the test…by someone involved in the creation of the actual exam • Find 85 practice questions based on the actual exam’s format and approach, along with comprehensive answers to help you gain understandingPublisher’s Note: While this book covers much of the information needed to prepare for Exam 730, a far more in-depth review of topics specifically related to Exam 730 can be found in the MC Press companion book: DB2 9 Fundamentals Certification Study Guide by Roger E. Sanders.Table of ContentsDB2 Product Fundamentals Environment Access and Security Database Objects Retrieving and Manipulating Database Objects Advanced SQL Coding Maintaining Data Recovery and Restart Data Sharing Using SQL in an Application Pogram Binding an Application Program Application Program Features Stored Procedures Accessing Distributed Data Advanced Functionality Locking and Concurrency Performance Monitoring and Tuning

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Book SynopsisIn DB2 9 for Linux, UNIX, and Windows Database Administration Upgrade Certification Study Guide , Roger E. Sanders--one of the world's leading DB2 authors and an active participant in the development of IBM's DB2 certification exams--covers everything a reader needs to know to pass the short--but notoriously challenging--DB2 9 for LUW DBA Certification Upgrade exam (Exam 736). This specialized study guide steps you through all of the topics that are covered on the exam, including server management, data placement, XML concepts, activity analysis, high availability, database security, and much more. Everything new to DB2 9 that you will need to know in order to successfully pass the exam is covered in this book. Taking and passing the DB2 9 for LUW DBA Certification Upgrade exam (Exam 736) provides validation that you have mastered DB2 9. Passing this exam also earns you the IBM Certified Database Administrator or Advanced Database Administrator certification. This concentrated guide includes an extensive set of practice questions in each chapter that closely models the actual exam, along with an answer key with a full description of why the answer is the correct one. No other source gives you this much help in passing the exam. With the DB2 9 for Linux, UNIX, and Windows Database Administration Upgrade Certification Study Guide , you will: Gain the knowledge necessary to pass the DB2 9 for LUW DBA Upgrade Certification exam (Exam 736) Discover the changes to DB2 9 that you'll need to know in order to be successful when taking the exam Receive an explanation of every topic included on the test...by someone involved in the creation of the actual exam Find 60 practice questions based on the actual exam's format and approach, along with comprehensive answers to the test questions to help you gain understandingTable of ContentsChapter 1: IBM DB2 9 Certification Chapter 2: Server Management Chapter 3: Data Placement Chapter 4: XML Concepts Chapter 5: Analyzing DB2 Activity Chapter 6: High Availability Chapter 7: Security

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Book SynopsisThrough a series of case studies from a variety of industries to show how customer experience analytics (CEA) is reshaping business, this book explores the technologies available to help businesses create a competitive advantage and real-time relationship with customers. This book provides a program based in business values that appeal to senior management and a solution architecture that utilizes the fast, intelligent, and productive capabilities of CEA. Exploring the internet's impact on consumer power, this book reflects on the sophistication of business markets in multisupplier management, electronic gateways, and customer and product data across the supply hierarchy.

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Book SynopsisProviding expert knowledge about the features in the new release of DB2 for z/OS, this extensive guide details the innovations of DB2 10’s SQL and pureXML enhancements—which increase productivity, enhance performance, and simplify application ports. DB2 for z/OS continues to be the undisputed leader in total system availability, scalability, security, and reliability at the lowest cost per transaction. This resource focuses on the features and functions of DB2 10 for IT, including improving operational efficiencies and reducing costs, as well as covering innovations in resiliency for business-critical information, rapid application and warehouse deployment for business growth, and enhanced business analytics and mathematical functions with QMF.

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Book SynopsisThe relational database-management system DB2 9.7 is given detailed and comprehensive treatment in this exam-preparation resource. Compiled from presentation material used in the popular “Crammer Course” at the IBM Information On Demand Conference, everything required for certification is presented here, including server management, design, business rules implementation, activity monitoring, security, and networking. An essential resource, this guide is helpful when studying to pass the official DB2 9.7 for LUW DBA certification exam.

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Book SynopsisMuch more than a simple certification study aid, this comprehensive 1,248 page book is designed to help you master all aspects of IBM DB2 database administration and prepare you to take and pass IBM's Certification Exams 611 and 311: Certified Database Administrator. Building on years of extensive hands-on experience, the authors step you through all the areas covered on the test. The book dives deep inside each certification topic: DB2 server management, physical design, business rules implementation, activity monitoring, utilities, high availability, security, and connectivity and networking. There is even a "crash course" chapter on DB2 10.5 features. Each chapter includes an extensive set of practice questions along with carefully explained answers. This book provides more than 400 practice questions and answers, more than 120 "flash cards" to help you study for the exam, and 50 step-by-step DB2 feature implementation procedures.Trade Review"This resource is designed to give the DB2 professional the information required in order to successfully obtain certification, or even to simply enhance their existing scope of DB2 knowledge. The authors have done an excellent job of distilling their many years of experience, both within the lab environment and within the live production environment into a logical, well-organized reference. Each section contains the fundamentals, plus valuable insights from the authors, and is backed up with sample exam questions, as well as detailed answers . . . . I am confident that with this guide, your certification will not be far away!" Eric Sheley, Global IT Director, FTSE 100 Global Consumer Goods Company

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Book SynopsisBringing a practitioner s view to big data analytics, this work examines the drivers behind big data, postulates a set of use cases, identifies sets of solution components, and recommends various implementation approaches. This work also addresses and thoroughly answers key questions on this emerging topic, including What is big data and how is it being used? How can strategic plans for big data analytics be generated? and How does big data change analytics architecture? The author, who has more than 20 years of experience in information management architecture and delivery, has drawn the material from a large breadth of workshops and interviews with business and information technology leaders, providing readers with the latest in evolutionary, revolutionary, and hybrid methodologies of moving forward to the brave new world of big data.

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Book SynopsisCelebrating the 30th anniversary of the first release of DB2, this book highlights the important milestones, capabilities, and impacts of the database management software for IBM s mainframe operating system. Special focus is given to IBM DB2 Analytics Accelerator, covering the key design and operational aspects that enable IBM DB2 for z/OS clients to benefit from faster performance, reduced CPU usage, and lower costs. The second half of the book discusses performance enhancements and cost-saving measures in the version 10 release and is rich with hints and tips for a successful upgrade. A special section on query performance and IBM DB2 Optimizer illustrates how DB2 10 addresses customer issues such as reducing total cost of ownership while maintaining stability and reliability. The final section is a collection of case studies in which DB2 10 for z/OS customers share their migration experiences and articulate the business benefits they are seeing since upgrading to the new release.

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Book SynopsisData is becoming the world's new "natural resource," transforming industries and professions across the board. Smart, innovative organizations know that data is the new basis of gaining a competitive advantage. DB2 for z/OS remains the leading database for storing mission-critical data. This book explains how DB2 for z/OS and supporting products enable businesses to use their data to gain a competitive advantage.

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Book SynopsisRoger E. Sanders, a leading DB2 author and an active participant in the development of DB2 certification exams, covers everything a reader needs to know to take and pass the DB2 10.5 DBA for LUW Upgrade from DB2 10.1 certification exam. This set of study notes takes the reader through each of the topics: DB2 server management; physical design; monitoring DB2 activity; high availability; and utilities. In addition, this book contains a complete practice exam with 60 questions, which closely models the actual 311 exam, along with a detailed answer key.

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Book SynopsisCombining multiple sensors in order to better grasp a tricky, or even critical, situation is an innate human reflex. Indeed, humans became aware, very early on, of the need to combine several of our senses so as to acquire a better understanding of our surroundings when major issues are at stake. On the basis of this need, we have naturally sought to equip ourselves with various kinds of artificial sensors to enhance our perceptive faculties. The association of multiple heterogeneous sensors provides a reliable and efficient situation assessment in difficult operational contexts, but imperfect local observations need to be managed in a suitable way (uncertainty, imprecision, incompleteness, unreliability, etc.). The theories of uncertainty make it possible to benefit from such information, but the implementation of these theories requires specific developments to meet the needs of multisensor data fusion. This book first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms (probability, fuzzy set theory, possibility theory, belief function theory), a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book. Furthermore, the implementation of these operators is illustrated and discussed within the framework of generic applications.Table of ContentsINTRODUCTION ix CHAPTER 1. MULTISENSOR DATA FUSION 1 1.1. Issues at stake 1 1.2. Problems 4 1.2.1. Interpretation and modeling of data 8 1.2.2. Reliability handling 10 1.2.3. Knowledge propagation 11 1.2.4. Matching of ambiguous data 12 1.2.5. Combination of sources14 1.2.6. Decision-making 16 1.3. Solutions 21 1.3.1. Panorama of useful theories 21 1.3.2. Process architectures 24 1.4. Position of multisensor data fusion 27 1.4.1. Peculiarities of the problem 27 1.4.2. Applications of multisensor data fusion 28 CHAPTER 2. REFERENCE FORMALISMS 31 2.1. Probabilities 31 2.2. Fuzzy sets 35 2.3. Possibility theory 39 2.4. Belief functions theory 43 2.4.1. Basic functions 44 2.4.2. A few particularly useful cases 47 2.4.3. Conditioning/deconditioning 49 2.4.4. Refinement/coarsening 50 CHAPTER 3. SET MANAGEMENT AND INFORMATION PROPAGATION 53 3.1. Fuzzy sets: propagation of imprecision 53 3.2. Probabilities and possibilities: the same approach to uncertainty 56 3.3. Belief functions: an overarching vision in terms of propagation 57 3.3.1. A generic operator: extension 58 3.3.2. Elaboration of a mass function with minimum specificity 61 3.3.3. Direct exploitation of the operator of extension 64 3.4. Example of application: updating of knowledge over time 66 CHAPTER 4. MANAGING THE RELIABILITY OF INFORMATION 71 4.1. Possibilistic view 72 4.2. Discounting of belief functions 73 4.3. Integrated processing of reliability 75 4.4. Management of domains of validity of the sources 77 4.5. Application to fusion of pixels from multispectral images 82 4.6. Formulation for problems of estimation 87 CHAPTER 5. COMBINATION OF SOURCES 91 5.1. Probabilities: a turnkey solution, Bayesian inference 92 5.2. Fuzzy sets: a grasp of axiomatics 94 5.3. Possibility theory: a simple approach to the basic principles 102 5.4. Theory of belief functions: conventional approaches 106 5.5. General approach to combination: any sets and logics 113 5.6. Conflict management 118 5.7. Back to Zadeh’s paradox 122 CHAPTER 6. DATA MODELING 127 6.1. Characterization of signals 127 6.2. Probabilities: immediate taking into account 130 6.3. Belief functions: an open-ended and overarching framework 131 6.3.1. Integration of data into the fusion process 132 6.3.2. Generic problem: modeling of Cij values 135 6.3.3. Modeling measurements with stochastic learning 139 6.3.4. Modeling measurements with fuzzy learning 144 6.3.5. Overview of models for belief functions 148 6.4. Possibilities: a similar approach 153 6.5. Application to a didactic example of classification 157 CHAPTER 7. CLASSIFICATION: DECISION-MAKING AND EXPLOITATION OF THE DIVERSITY OF INFORMATION SOURCES 165 7.1. Decision-making: choice of the most likely hypothesis 166 7.2. Decision-making: determination of the most likely set of hypotheses 168 7.3. Behavior of the decision operator: some practical examples 171 7.4. Exploitation of the diversity of information sources: integration of binary comparisons 175 7.5. Exploitation of the diversity of information sources: classification on the basis of distinct but overlapping sets 179 7.6. Exploitation of the diversity of the attributes: example of application to the fusion of airborne image data 189 CHAPTER 8. SPATIAL DIMENSION: DATA ASSOCIATION 193 8.1. Data association: a multiform problem, which is unavoidable in multisensor data fusion 194 8.2. Construction of a general method for data association 197 8.3. Simple example of the implementation of the method 203 CHAPTER 9. TEMPORAL DIMENSION: TRACKING 211 9.1. Tracking: exploitation of the benefits of multisensory data fusion 211 9.2. Expression of the Bayesian filter 218 9.2.1. Statistical gating 218 9.2.2. Updating 219 9.2.3. Prediction 220 9.3. Signal discrimination process 221 9.3.1. Fusion at the level of each resolution cell 222 9.3.2. Fusion at the level of the validation gate 224 9.3.3. Overview of a practical implementation of the discrimination method 226 9.4. Extensions of the basic MSF 228 9.4.1. Data association 228 9.4.2. Joint tracking of multiple targets 229 9.4.3. Multi-model filtering 231 9.5. Examples of application 232 9.5.1. Extraction power 233 9.5.2. Handling of unfamiliar signatures 235 9.5.3. Tracking on spatially ambiguous observations 238 CONCLUSION 241 BIBLIOGRAPHY 249 INDEX 257

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