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Software Engineering

Our new Indian original book on software engineering covers conventional as well as current methodologies of software development to explain core concepts, with a number of case studies and worked-out examples interspersed among the chapters. Current industry practices followed in development, such as computer aided software engineering, have also been included, as are important topics like ‘Widget based GUI' and ‘Windows Management System'. The book also has coverage on interdisciplinary topics in software engineering that will be useful for software professionals, such as ‘quality management', ‘project management', ‘metrics' and ‘quality standards'. Features Covers both function oriented as well as object oriented (OO) approach Emphasis on emerging areas such as ‘Web engineering’, ‘software maintenance’ and ‘component based software engineering’ A number of line diagrams and examples Case Studies on the ATM system and milk dispenser Includes multiple-choice, objective-type questions and frequently asked questions with answers.

The book also has coverage on interdisciplinary topics in software engineering that will be useful for software professionals, such as ‘quality management', ‘project management', ‘metrics' and ‘quality standards'.

FIQIH RIBA: Studi Komprehensif Tentang Riba Sejak Zaman Klasik Hingga Modern

Fundamentals of Database Management Systems

Gillenson's new edition of Fundamentals of Database Management Systems provides concise coverage of the fundamental topics necessary for a deep understanding of the basics. In this issue, there is more emphasis on a practical approach, with new "your turn" boxes and much more coverage in a separate supplement on how to implement databases with Access. In every chapter, the author covers concepts first, then show how they're implemented in continuing case(s.) "Your Turn" boxes appear several times throughout the chapter to apply concepts to projects. And "Concepts in Action" boxes contain examples of concepts used in practice. This pedagogy is easily demonstrable and the text also includes more hands-on exercises and projects and a standard diagramming style for the data modeling diagrams. Furthermore, revised and updated content and organization includes more coverage on database control issues, earlier coverage of SQL, and new coverage on data quality issues.

This pedagogy is easily demonstrable and the text also includes more hands-on exercises and projects and a standard diagramming style for the data modeling diagrams.

Concepts of Database Management

CONCEPTS OF DATABASE MANAGEMENT fits perfectly into any introductory database course for information systems, business or CIS programs. This concise text teaches SQL in a database-neutral environment with all major topics being covered, including E-R diagrams, normalization, and database design. Now in its seventh edition, CONCEPTS OF DATABASE MANAGEMENT prepares students for success in their field using real-world cases addressing current issues such as database design, data integrity, concurrent updates, and data security. Special features include detailed coverage of the relational model (including QBE and SQL), normalization and views, database design, database administration and management, and more. Advanced topics covered include distributed databases, data warehouses, stored procedures, triggers, data macros, and Web databases. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

This concise text teaches SQL in a database-neutral environment with all major topics being covered, including E-R diagrams, normalization, and database design.

Legislasi fikih ekonomi dan penerapannya dalam produk perbankan syariah di Indonesia

On the implementation of Islamic economic laws in banking sector based on sharia in Indonesia.

On the implementation of Islamic economic laws in banking sector based on sharia in Indonesia.

Methods for Statistical Data Analysis of Multivariate Observations

A practical guide for multivariate statistical techniques-- nowupdated and revised In recent years, innovations in computer technology and statisticalmethodologies have dramatically altered the landscape ofmultivariate data analysis. This new edition of Methods forStatistical Data Analysis of Multivariate Observations explorescurrent multivariate concepts and techniques while retaining thesame practical focus of its predecessor. It integrates methods anddata-based interpretations relevant to multivariate analysis in away that addresses real-world problems arising in many areas ofinterest. Greatly revised and updated, this Second Edition provides helpfulexamples, graphical orientation, numerous illustrations, and anappendix detailing statistical software, including the S (or Splus)and SAS systems. It also offers * An expanded chapter on cluster analysis that covers advances inpattern recognition * New sections on inputs to clustering algorithms and aids forinterpreting the results of cluster analysis * An exploration of some new techniques of summarization andexposure * New graphical methods for assessing the separations among theeigenvalues of a correlation matrix and for comparing sets ofeigenvectors * Knowledge gained from advances in robust estimation anddistributional models that are slightly broader than themultivariate normal This Second Edition is invaluable for graduate students, appliedstatisticians, engineers, and scientists wishing to usemultivariate techniques in a variety of disciplines.

This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor.

An Introduction to Applied Multivariate Analysis with R

The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned.

The Multivariate Normal Distribution

The multivariate normal distribution has played a predominant role in the historical development of statistical theory, and has made its appearance in various areas of applications. Although many of the results concerning the multivariate normal distribution are classical, there are important new results which have been reported recently in the literature but cannot be found in most books on multivariate analysis. These results are often obtained by showing that the multivariate normal density function belongs to certain large families of density functions. Thus, useful properties of such families immedi ately hold for the multivariate normal distribution. This book attempts to provide a comprehensive and coherent treatment of the classical and new results related to the multivariate normal distribution. The material is organized in a unified modern approach, and the main themes are dependence, probability inequalities, and their roles in theory and applica tions. Some general properties of a multivariate normal density function are discussed, and results that follow from these properties are reviewed exten sively. The coverage is, to some extent, a matter of taste and is not intended to be exhaustive, thus more attention is focused on a systematic presentation of results rather than on a complete listing of them.

This book attempts to provide a comprehensive and coherent treatment of the classical and new results related to the multivariate normal distribution.