This research note shows that the scope of conceptual data modeling patterns can be identified, clarified, and explicitly represented by positioning them into an ontology-based framework. A clear and explicit definition of scope could help deciding which patterns match which parts of the domain to be represented. The authors demonstrate their argument by positioning existing conceptual data modeling patterns into a two-dimensional structuring framework that is constructed using two ontology-derived ‘benchmark’ patterns: an enterprise pattern for representing transactions (derived from the REA domain ontology) and an abstraction pattern for representing reality at different levels of abstraction (derived from the UFO foundational ontology). By means of an application scenario dealing with the conceptual design of a transactional enterprise database, the authors illustrate how the framework can be used to evaluate the relevancy and completeness of candidate patterns with respect to the problem at hand.

This paper proposes a cooperative query answering approach that relaxes query conditions to provide approximate answers by utilizing similarity relationships between data values. The proposed fuzzy abstraction hierarchy (FAH) represents a similarity relationship based on the integrated notion of data abstraction and fuzzy relations. Based on FAH, the authors develop query relaxation operators like query generalization, approximation, and specialization of a value. Compared with existing approaches, FAH supports more effective information retrieval by processing various kinds of cooperative queries through elaborate relaxation control and providing ranked query results according to fitness scores. Moreover, FAH reduces maintenance cost by decreasing the number of similarity relationships to be managed.

Privacy concerns and practices, especially those dealing with the acquisition and use of consumer personal information by corporations, are at the forefront of business and social issues associated with the information age. This research examines the privacy policies of large U.S. companies to assess the substance and quality of their stated information practices. Six factors are identified that indicate the extent to which a firm is dependent upon consumer personal information, and therefore more likely to develop high quality privacy statements. The study’s findings provide practical and theoretical implications for information privacy issues, particularly for consumers who need to determine whether or not to disclose their personal identifying information to firms. The results illustrate the complexity involved in managing personal private information.

Extensible markup language (XML) has become a standard for persistent storage and data interchange via the Internet due to its openness, self-descriptiveness, and flexibility. This article proposes a systematic approach to reverse engineer arbitrary XML documents to their conceptual schema–extended DTD graphs?which is a DTD graph with data semantics. The proposed approach not only determines the structure of the XML document, but also derives candidate data semantics from the XML element instances by treating each XML element instance as a record in a table of a relational database. One application of the determined data semantics is to verify the linkages among elements. Implicit and explicit referential linkages are among XML elements modeled by the parent-children structure and ID/IDREF(S) respectively. As a result, an arbitrary XML document can be reverse engineered into its conceptual schema in an extended DTD graph format.

Efficient XML filtering has been the fundamental technique in recent Web service and XML publish/subscribe applications. In this article, we consider the problem of filtering a streaming XML data efficiently against a large number of branch XPath queries. To improve the performance of XML filtering, branch queries are grouped into similar queries, and the common paths between queries in the same group are identified. After performing structural matching of queries, queries are organized in a way that multiple queries can be evaluated simultaneously in the post-processing phase. In the post-processing phase, join operations are executed in a pipeline fashion, and intermediate join results are shared amongst the queries in the same group. As a result, the total number of join operations performed in the post-processing phase is significantly reduced. In addition, we also present how to efficiently return all matching elements for each matching branch query. Experiments show that our proposal is efficient and scalable compared to previous work.

Use case modeling in the Unified Modeling Language (UML) is a popular text-based tool for systems analysis and design. Use cases can be used with or without supporting use case diagrams. This paper uses an experiment to explore the effectiveness of including a use case diagram with a set of use cases. The Cognitive Theory of Multimedia Learning is used to hypothesize that the use case diagram improves the effectiveness of use cases for novice users by providing visual cues aiding model viewers in selecting and integrating relevant information. The level of understanding developed by participants viewing either uses cases or use cases with a supporting use case diagram was measured using comprehension, retention, and problem solving tasks. Results showed that participants viewing the use cases with the supporting diagram developed a significantly higher level of understanding, as measured by performance on the problem solving task, than participants provided with use cases alone. This analysis suggests practitioners should consider combining a visual representation, such as a use case diagram, with text-based use cases to achieve higher levels of understanding in persons viewing these descriptions.

This study centers on the accuracy dimension of information quality and models the relationship between input accuracy and output accuracy in a popular class of applications. These applications consist of dichotomous decisions that are implemented through conjunction of selected criteria. In particular, the model provides the tools for quantifying the effect of errors in each decision input on the accuracy of a decision. Application of the model is illustrated through the example of a residential real estate database, where users’ preferences are captured by conjunctive decision rules. This example demonstrates how the new model can be utilized for quantifying the aggregate damage that errors in different database attributes inflict on property selection decisions. Finally, this paper reports on an initial empirical validation of the proposed model through a series of Monte Carlo simulations. Numerical estimates of the model that have been developed through this inquiry can be useful for data and information quality assessments or policy-making purposes. Mainly, they can be integrated into cost-benefit analyses that assess alternative data accuracy enhancements or process and system designs.

Query relaxation is one of the crucial components for approximate query answering. Query relaxation has extensively been investigated in terms of categorical data; few studies, however, have been effectively established for both numerical and categorical data. In this article, we develop a query relaxation method by exploiting hierarchical quantified data abstraction, and a novel method is proposed to quantify the semantic distances between the categorical data so that the query conditions for categorical data are effectively relaxed. We additionally introduce query relaxation algorithms to modify the approximate queries into ordinary queries, which are followed by a series of examples to represent the modification process. Our method outperformed the conventional approaches for the various combinations of complex queries with respect to the cost model and the number of child nodes.

Ontology has recently received considerable attention in the knowledge management community. This article discusses the needs of ontology development for data mining. Based on a domain analysis of knowledge representations in data mining, it proposes a generic structure of ontologies for data mining. Furthermore, this article specifies the unique ontology resources of the subdomain of innovative data mining with incomplete data. A project on an ontology-based data mining system for discovering knowledge from incomplete data demonstrates the effectiveness of ontology in knowledge management.

Event monitoring and active behaviors are important aspects in many software systems and application domains, not only in database management systems. In this article, we propose an event-condition-action (ECA) approach that spans from application data to application components and behaviors. Starting from an exception manager we previously developed in the context of a workflow management system, we derived an autonomous active component capable of handling a variety of events and enacting actions in response to detected events. The ECA server runs as an autonomous engine and can be seamlessly integrated with existing systems, thus enhancing the systems’ functionalities and maintainability by separating active and nonactive design concerns.