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Fully 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, Oracle, or other type of DBMS. One chapter is devoted to illustrating how Microsoft Access can be used to create user interfaces to review and update underlying tables in that database as well as tables residing in SQL Server or Oracle. For users involved with Cyber activity or support, one chapter illustrates how to extract records of interest from a log file using PERL, then shows how to load these extracted records into one or more SQL Server “tracking” tables adding status flags for analysts to use when reviewing activity of interest. These status flags are used to flag/mark collected records as “Reviewed”, “Pending” (currently being analyzed) and “Resolved”. The last chapter then shows how to build a web-based GUI using PHP to query these tracking tables and allow an analyst to review new activity, flag items that need to be investigated, and finally flag items that have been investigated and resolved. Note that the book has complete code/scripts for both PERL and the PHP GUI.
Typically, analysis, development, and database teams work for different business units, and use different design notations. With UML and the Rational Unified Process (RUP), however, they can unify their efforts -- eliminating time-consuming, error-prone translations, and accelerating software to market. In this book, two data modeling specialists from Rational Software Corporation show exactly how to model data with UML and RUP, presenting proven processes and start-to-finish case studies. The book utilizes a running case study to bring together the entire process of data modeling with UML. Each chapter dissects a different stage of the data modeling process, from requirements through implementation. For each stage, the authors cover workflow and participants' roles, key concepts, proven approach, practical design techniques, and more. Along the way, the authors demonstrate how integrating data modeling into a unified software design process not only saves time and money, but gives all team members a far clearer understanding of the impact of potential changes. The book includes a detailed glossary, as well as appendices that present essential Use Case Models and descriptions. For all software team members: managers, team leaders, systems and data analysts, architects, developers, database designers, and others involved in building database applications for the enterprise.
This book provides a practical explanation of the Analytic Hierarchy Process (AHP) method and its implementation in a decision support system to determine the scholarship recipients in a university. The discussion in this book is presented step-by-step, gradually, so that it will help readers understand every concept and stage of the AHP method and its implementation in decision support systems. This book is suitable for students, scholars, and researchers who want to implement the AHP method to build decision support systems using Python GUI and MySQL. The discussion in this book will provide readers with an understanding of the AHP method and how it works. Readers will be guided through each step of determining priority order using the AHP method through a case study accompanied by mathematical calculations. In addition, readers will also be guided step-by-step in implementing the AHP method in a decision support system to determine the scholarship recipients using Python GUI and MySQL. Readers will be guided step-by-step in designing a decision support system and realizing each design made using Python GUI and MySQL. The ultimate goal of this book is to enable readers to build a decision support system application independently by applying the AHP method to determine the scholarship recipients in a college using Python GUI and MySQL.
The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers.
Although many web professionals will have incorporated a database into a web site before, they may not have much experience of designing them - this book will teach you all you need to know about designing a database for use with a web site or web application. From first principles to designing a successful web database, this book will show you how to get the most out of database design. From the Publisher Unlike other database design books in the market, this one focuses on design of databases for use on the Web. Web databases benefit from good general database design principles, but also have their own set of caveats, which must be considered for their design to be truly successful. This book covers both the general, and the web-specific database principles.
This new book aims to provide both beginners and experts with a completely algorithmic approach to data analysis and conceptual modeling, database design, implementation, and tuning, starting from vague and incomplete customer requests and ending with IBM DB/2, Oracle, MySQL, MS SQL Server, or Access based software applications. A rich panoply of s
Clinical data management (CDM) has changed from being an essentially clerical task in the late 1970s and early 1980s to a highly computerized, highly specialized field today. And clinical data manages have had to adapt their data management systems and processes accordingly. Practical Guide to Clinical Data Management steers you through a basic understanding of the role of data management in clinical trials and includes more advanced topics such as CDM systems, SOPs, and quality assurance. This book helps you ensure GCP, manage laboratory data, and deal with the kinds of clinical data that can cause difficulties in database applications. With the tools this book provides, you'll learn how to: Ensure that your DMB system is in compliance with federal regulations Build a strategic data management and databsing plan Track and record CRFs Deal with problem data, adverse event data, and legacy data Manage and store lab data Identify and manage discrepancies Ensure quality control over reports Choose a CDM system that is right for your company Create and implement a system validation plan and process Set up and enforce data collection standards Develop test plans and change control systems This book is your guide to finding the most successful and practical options for effective clinical data management.