In this paper, a terminological framework is provided for describing different transaction-oriented recovery schemes for database systems in a conceptual rather than an implementation-dependent way. By introducing the terms materialized database, propagation strategy, and checkpoint, we obtain a means for classifying arbitrary

The purpose of this paper is to show that logic provides a convenient formalism for studying classical database problems. There are two main parts to the paper, devoted respectively to conventional databases and deductive databases. In the first part, we focus on query languages, integrity modeling and maintenance, query optimization, and data

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Abstiuct-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 de-velops 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 responsibili-ties, operation and assurance costs, research and development, production, distribution, personnel management, and legal func-tion. The analysis reveals that most research efforts focus on op-eration and assurance costs, research and development, and pro-duction of data products. Unexplored research topics and unre-solved issues are identified and directions for future research provided. Index Terms-Data quality, data manufacturing, data product,

This paper studies heterogeneous database systems from the multiple (poly) source @rrt) perspective. It aims at addressing issues such as “where is the data from ” and “which intermediate data sources were used to arrive at that data ”- issues which are critical to many users in utilizing information composed from multiple sources. Specifically, it presents a polygen model for resolving the Data Source Tagging and Intermediate Source Tagging problems. Secondly, it presents a data-driven query translation mechanism for mapping a polygen query into a set of local queries dynamically. A concrete example is also provided to exemplify polygen query processing. The significance of this paper lies not only in a precise characterization of a practical problem and a solution per se, but also in the establishment of a foundation for resolving many other critical research issues such as domain mismatch, semantic reconciliation, and data conflict amongst data retrieved from different sources. In a federated database environment with hundreds of databases, all of these issues are critical to their effective USt!. I.

Design work and design knowledge in Information Systems (IS) is important for both research and practice. Yet there has been comparatively little critical attention paid to the problem of specifying design theory so that it can be communicated, justified, and developed cumulatively. In this essay we focus on the structural components or anatomy of design theories in IS as a special class of theory. In doing so, we aim to extend the work of Walls, Widemeyer and El Sawy (1992) on the specification of information systems design theories (ISDT), drawing on other streams of thought on design research and theory to provide a basis for a more systematic and useable formulation of these theories. We identify eight separate components of design theories: (1) purpose and scope, (2) constructs, (3) principles of form and function, (4) artifact mutability, (5) testable propositions, (6) justificatory knowledge (kernel theories), (7) principles of implementation, and (8) an expository instantiation. This specification includes components missing in the Walls et al. adaptation of Dubin (1978) and Simon (1969) and also addresses explicitly problems associated with the role of instantiations and the specification of design theories for methodologies and interventions as well as for products and applications. The essay is significant as the unambiguous establishment of design knowledge as theory gives a sounder base for arguments for the rigor and legitimacy of IS as an applied discipline and for its continuing progress. A craft can proceed with the copying of one example of a design artifact by one artisan after another. A discipline cannot.

Data modelling reveals the internal structure of an information system, abstracting away from details of the physical representation. We show that entity-relationship modelling, a well-tried example of a data-modelling technique, can be applied to both interactive and non-interactive information artefacts in the domain of HCI. By extending the conventional ER notation slightly (to give ERMIA, Entity-Relationship Modelling for Information Artefacts) it can be used to describe differences between different representations of the same information, differences between user's conceptual models of the same device, and the structure and update requirements of distributed information in a worksystem. It also yields symbolic-level estimates of Card et al.'s (1994) index of `cost-of-knowledge' in an informa- tion structure, plus a novel index, the `cost-of-update'; these symbolic estimates offer a useful complement to the highly detailed analyses of time costs obtainable from GOMS-like...

In cooperative information systems, the quality of data exchanged and provided by different data sources is extremely important. A lack of attention to data quality can imply data of low quality to spread all over the cooperative system. At the same time, improvement can be based on comparing data, correcting them and thus disseminating high quality data. In this paper, we present an architecture for managing data quality in cooperative information systems, by focusing on two specific modules, the Data Quality Broker and the Quality Notification Service. The Data Quality Broker allows for querying and improving data quality values. The Quality Notification Service is specifically targeted to the dissemination of changes on data quality values.

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To fight terrorism successfully, the quality of data must be considered to avoid garbage-in-garbage-out. Research has shown that data quality (DQ) goes beyond accuracy to include dimensions such as believability, timeliness, and accessibility. In collecting, processing, and analyzing a much broader array of data than we do currently, therefore, a comprehensive approach must be developed to ensure that DQ is incorporated in determining the most probable current or future scenario for preemption, national security warning and decision making. Additional data such as who was the data source, when was the data made available, how, where, and why also need to be included to judge the quality of the information assembled from these data.