Developing Quality Complex Database Systems: Practices, Techniques and Technologies - Softcover

Becker, Shirley A.

Softcover

ISBN 10: 1878289888 ISBN 13: 9781878289889

Publisher: Igi Global, 2000

Synopsis

The objective of Developing Quality Complex Database Systems is to provide opportunities for improving today's database systems using innovative development practices, tools and techniques. Each chapter of this book will provide insight into the effective use of database technology through models, case studies or experience reports. An emphasis is placed on organizational and management issues associated with the use of such technology inclusive of lessons learned and best practices.

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Excerpt. © Reprinted by permission. All rights reserved.

Database practices, techniques, and technologies continue to play a significant role in riding the increasingly turbulent wave of technological advances. The Internet is having a major impact on information sharing in a global marketplace. Advances in science and engineering fields have resulted in an explosion of information that must be effectively modeled and managed. Legacy software systems have vast amounts of data ready for analysis and interpretation. These are just a few examples of the opportunities and challenges associated with today's database systems. Effective mechanisms for collection, storage, retrieval, analysis, and dissemination of information are needed to take advantage of technological breakthroughs. Data complexity issues need to be addressed such as, security, maintainability, completeness, and correctness, in order to minimize the risks associated with these new technologies. Innovations in database practices, techniques, and technology are needed to meet the increasing challenges of this new decade.

The objective of this book is to share innovative and groundbreaking database concepts as proposed by the contributing authors. The concepts proposed in these chapters provide a foundation for continuous improvements in the database field. The diversity of coverage includes quality and organizational issues, measurement systems, design and implementation methods, data warehousing and mining techniques, data modeling and re-engineering techniques, security, and enhanced query capabilities. Each of these chapters is briefly described.

In the chapter entitled, "Organizational Concepts and Measures for the Evaluation of Data Modeling," Ronald Maier presents an organizational perspective on data modeling. His discussion of the evaluation of data modeling is based on existing theoretical approaches and his empirical studies. The chapter proposes that data modeling should include both project-driven activities and enterprise-wide activities inclusive of long-term goals. It also points out the need for focusing on organizational issues associated with data modeling inclusive of process and product perspectives of quality.

In "Dimensions of Database Quality," John Hoxmeier reminds us that data quality is a critical issue because databases are part of virtually all conventional and ebusiness applications. He points out the need for a comprehensive set of quality dimensions in order to be successful in the development of high-quality database systems. In addition to process and data factors, it is proposed that quality dimensions include model and behavioral factors, as these are not typically part of data quality assessment. A framework is presented, which allows for an assessment of process, data, model, and behavior quality dimensions. A test case is used to illustrate the application of the proposed framework.

Coral Calero, Mario Piattini, and Marcela Genero address an important but mostly overlooked issue of metrics for assessing database complexity. They propose internal measures in their chapter, "Metrics for Controlling Database Complexity," for assessing the quality of database systems. These measures characterize the complexity of a database in order to promote data quality. The focus of the chapter is on the measurement of complexity that affects the maintainability of the relational, object-relational, and active database schemas. The authors are expanding upon their work in collaboration with industry and public organizations.

In "Íntegrating Hypermedia Functionality into Database Applications," Bhaumik et al., describe their research efforts at exploring all aspects of hypermedia support for database applications. They propose that a dynamic hypermedia engine (DHE) be used to automate features associated with database systems inclusive of web technology. One such feature is the automatic generation of links based on the database's conceptual schema with its original specification. This technology allows for the developer to specify which kinds of database elements are related to diverse elements in the same application, other database applications, or other software systems. It is proposed that data warehousing and data mining be incorporated into this technology. Many of today's database systems require the processing of large volumes of data in order to support the discovery of new knowledge. In "Optimization of the Knowledge Discovery Process in Very Large Databases," Mehdi Owrang discusses how today's vast amount of data makes the discovery process computationally expensive. It is proposed that domain knowledge be used to reduce the size of the database and to optimize the hypothesis thus eliminating implied, unnecessary, and redundant conditions. The resulting benefits include greater efficiency and the discovery of meaningful, non-redundant, and consistent rules.

Data warehousing is the focus of Ladjel Bellatreche, Kamalakar Karlapalem, and Mukesh Mohania's chapter on, "Some Issues in Design of Data Warehousing Systems." The authors tell us that data warehousing can assist intelligent decision-making in order to improve the functioning of an organization. Several trade-offs associated with data warehousing designs are discussed inclusive of materialized views, partitioning a data warehouse, and index selection to efficiently execute queries. A study is described which addresses these issues and the findings are shared.

"Data Mining for Supply Chain Management in Complex Networks: Concepts, Methodology and Applications" describes data mining concepts, its methodology, and its application in the context of supply chain management of complex networks. Manoj Singh and Mahesh Raisinghani help us make sense of data mining as a set of techniques used to uncover previously obscure or unknown patterns and relationships in very large databases. The authors describe supply chain management with its data complexity, which can be facilitated by data mining.

Relational databases and the current SQL standard are poorly suited to support hierarchical data. Ido Millet in "Accommodating Hierarchies in Relational Databases," describes techniques to address the data retrieval and maintenance problems posed by hierarchical data structures. Data denormalization is proposed as a means of addressing these problems. But there are costs associated with this approach. The chapter describes how these costs can be avoided using processing logic in the form of triggers.

In summary, this book offers an exciting opportunity to find out about current practices, techniques and technologies to meet the needs of today's software and information systems.

Edition details

• Publisher: Igi Global
• Publication date: 2000
• Language: English
• ISBN 10: 1878289888
• ISBN 13: 9781878289889
• Binding: Paperback
• Edition number: 1
• Number of pages: 374