Veda C. Storey

Business intelligence and analytics: from big data to big impact

Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.

A framework for analysis of data quality research

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. >

An ontological analysis of the relationship construct in conceptual modeling

Conceptual models or semantic data models were developed to capture the meaning of an application domain as perceived by someone. Moreover, concepts employed in semantic data models have recently been adopted in object-oriented approaches to systems analysis and design. To employ conceptual modeling constructs effectively, their meanings have to be defined rigorously. Often, however, rigorous definitions of these constructs are missing. This situation occurs especially in the case of the relationship construct. Empirical evidence shows that use of relationships is often problematical as a way of communicating the meaning of an application domain. For example, users of conceptual modeling methodologies are frequently confused about whether to show an association between things via a relationship, an entity, or an attribute. Because conceptual models are intended to capture knowledge about a real-world domain, we take the view that the meaning of modeling constructs should be sought in models of reality. Accordingly, we use ontology, which is the branch of philosophy dealing with models of reality, to analyze the meaning of common conceptual modeling constructs.Our analysis provides a precise definition of several conceptual modeling constructs. Based on our analysis, we derive rules for the use of relationships in entity-relationship conceptual modeling. Moreover, we show how the rules resolve ambiguities that exist in current practice and how they can enrich the capacity of an entity-relationship conceptual model to capture knowledge about an application domain.

Reverse engineering of relational databases: extraction of an EER model from a relational database

Abstract A methodology for extracting an extended Entity-Relationship (EER) model from a relational database is presented. Through a combination of data schema and data instance analysis, an EER model is derived which is semantically richer and more comprehensible for maintenance and design purposes than the original database. Classification schemes for relations and attributes necessary for the EER model extraction are derived and justified. These have been demonstrated to be implementable in a knowledge-based system; a working prototype system which does so is briefly discussed. In addition, cases in which human input is required are also clearly identified. This research also illustrates that the database reverse engineering process can be implemented at a high level of automation.

A semiotic metrics suite for assessing the quality of ontologies

A suite of metrics is proposed to assess the quality of an ontology. Drawing upon semiotic theory, the metrics assess the syntactic, semantic, pragmatic, and social aspects of ontology quality. We operationalize the metrics and implement them in a prototype tool called the Ontology Auditor. An initial validation of the Ontology Auditor on the DARPA Agent Markup Language (DAML) library of domain ontologies indicates that the metrics are feasible and highlights the wide variation in quality among ontologies in the library. The contribution of the research is to provide a theory-based framework that developers can use to develop high quality ontologies and that applications can use to choose appropriate ontologies for a given task.

Understanding semantic relationships

To develop sophisticated database management systems, there is a need to incorporate more understanding of the real world in the information that is stored in a database. Semantic data models have been developed to try to capture some of the meaning, as well as the structure, of data using abstractions such as inclusion, aggregation, and association. Besides these well-known relationships, a number of additional semantic relationships have been identified by researchers in other disciplines such as linguistics, logic, and cognitive psychology. This article explores some of the lesser-recognized semantic relationships and discusses both how they could be captured, either manually or by using an automated tool, and their impact on database design. To demonstrate the feasibility of this research, a prototype system for analyzing semantic relationships, called the Semantic Relationship Analyzer, is presented.

Ontologies for conceptual modeling: their creation, use, and management

Although ontologies have been proposed as an important and natural means of representing real world knowledge for the development of database designs, most ontology creation is not carried out systematically. To be truly useful, a repository of ontologies, organized by application domain is needed, along with procedures for creating and integrating ontologies into database design methodologies. This research proposes a methodology for creating and managing domain ontologies. An architecture for an ontology management system is presented and implemented in a prototype. Empirical validation of the prototype demonstrates the effectiveness of the research.

A semantic-based approach to component retrieval

There continues to be a great deal of pressure to design and develop information systems within a short period of time. This urgency has reinvigorated research on software reuse, particularly in component based software development. One of the major problems associated with component-based development is the difficulty in searching and retrieving reusable components that meet the requirement at hand. In part, this problem exists because of the lack of sophisticated query methods and techniques. In this research, a semantic-based approach to component retrieval is presented as a solution to this problem. This approach makes use of domain models containing the objectives, processes, actions, actors, and, an ontology of domain terms, their definitions, and relationships with other domain-specific terms. A reuse repository is developed that contains the components relevant for the creation of new applications, along with their attributes and methods. The natural language interface, domain model, and reusable repository are implemented in a prototype that uses Web and JavaBeans technologies. A sample session is provided for an online auction application to illustrate the usefulness of the proposed approach.

Conceptual Modeling — ER 2000

Model management is a framework for supporting meta-data related applications where models and mappings are manipulated as first class objects using operations such as Match, Merge, ApplyFunction, and Compose. To demonstrate the approach, we show how to use model management in two scenarios related to loading data warehouses. The case study illustrates the value of model management as a methodology for approaching meta-data related problems. It also helps clarify the required semantics of key operations. These detailed scenarios provide evidence that generic model management is useful and, very likely, implementable.

The role of domain ontologies in database design: An ontology management and conceptual modeling environment

Database design is difficult because it involves a database designer understanding an application and translating the design requirements into a conceptual model. However, the designer may have little or no knowledge about the application or task for which the database is being designed. This research presents a methodology for supporting database design creation and evaluation that makes use of domain-specific knowledge about an application stored in the form of domain ontologies. The methodology is implemented in a prototype system, the Ontology Management and Database Design Environment. Initial testing of the prototype illustrates that the incorporation and use of ontologies is effective in creating entity-relationship models.