The quality of data in relational databases is often uncertain, and the relationship between the quality of the underlying base tables and the set of potential query results, a type of information product (IP), that could be produced from them has not been fully investigated. This paper provides a basis for the systematic analysis of the quality of such IPs. This research uses the relational algebra framework to develop estimates for the quality of query results based on the quality estimates of samples taken from the base tables. Our procedure requires an initial sample from the base tables; these samples are then used for all possible information IPs. Each specific query governs the quality assessment of the relevant samples. By using the same sample repeatedly, our approach is relatively cost effective. We introduce the reference-table procedure, which can be used for quality estimation in general. In addition, for each of the basic algebraic operators, we discuss simpler procedures that may be applicable. Special attention is devoted to the join operation. We examine various, relevant statistical issues, including how to deal with the impact on quality of missing rows in base tables. Finally, we address several implementation issues related to sampling.

Despite the established theory and the history of the practical use of integrity rules, data quality problems, which should be solvable using data integrity rules, persist in organizations. One effective mechanism to solve this problem is to embed data integrity in a continuous data quality improvement process. The result is an iterative data quality improvement process as data integrity rules are defined, violations of these rules are measured and analyzed, and then the rules are redefined to reflect the dynamic and global context of business process changes. Using action research, we study a global manufacturing company that applied these ideas for improving data quality as it built a global data warehouse. This research merges data integrity theory with management theories about quality improvement using a data quality lens, and it demonstrates the usefulness of the combined theory for data quality improvement.

Large data volumes, widely distributed data sources, and multiple stakeholders characterize typical e-business settings. Mobile and wireless technologies have further increased data volumes, further distributed the data sources, while permitting access to data anywhere, anytime. Such environments empower and necessitate decision-makers to act/react quicker to all decision-tasks including mission-critical ones. Decision-support in such environments demands efficient data quality management. This paper presents a framework for managing data quality in such environments using the information product approach. It includes a modeling technique to explicitly represent the manufacture of an information product, quality dimensions and methods to compute data quality of the product at any stage in the manufacture, and a set of capabilities to comprehensively manage data quality and implement total data quality management. The paper also posits the notion of a virtual business environment to support dynamic decision-making and describes the role of the data quality framework in this environment.

Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided.>

A set or premises, terms, and definitions for data quality management are established, and a step-by-step methodology for defining and documenting data quality parameters important to users is developed. These quality parameters are used to determine quality indicators about the data manufacturing process, such as data source creation time, and collection method, that are tagged to data items. Given such tags, and the ability to query over them, users can filter out data having undesirable characteristics. The methodology provides a concrete approach to data quality requirements collection and documentation. It demonstrates that data quality can be an integral part of the database design process. A perspective on the migration towards quality management of data in a database environment is given.>

The issue of joining information about the same instance across disparate databases in a composite information system (CIS) environment is discussed. A technique called interdatabase instance identification is presented that is a combination of database management systems and artificial intelligence techniques. Common attributes in the disparate databases are applied first to reduce the number of potential candidates for the same instance. Other attributes in these databases, auxiliary databases, and inferencing rules are utilized next to identify the same instance. A detailed example of the interdatabase instance identification technique is presented using an operational research prototype.>

A performance analysis methodology using certain aspects of queueing theory to evaluate computer system speed performance is presented. This methodology specifically focuses on modeling multiprocessor computer systems with unbalanced flows (i.e., number of transactions leaving a server is not the same as number of transactions entering that server) due to asynchronously spawned parallel tasks. This unbalanced flow phenomenon, which has a significant effect on performance, cannot be solved analytically by classical queueing network models.