On the Discovery of Semantically Meaningful SQL Constraints from Armstrong Samples: Foundations, Implementation, and Evaluation

A database is said to be C-Armstrong for a finite set Σ of data dependencies in a class C if the database satisfies all data dependencies in Σ and violates all data dependencies in C that are not implied by Σ. Therefore, Armstrong databases are concise, user-friendly representations of abstract data dependencies that can be used to judge, justify, convey, and test the understanding of database design choices. Indeed, an Armstrong database satisfies exactly those data dependencies that are considered meaningful by the current design choice Σ. Structural and computational properties of Armstrong databases have been deeply investigated in Codd’s Turing Award winning relational model of data. Armstrong databases have been incorporated in approaches towards relational database design. They have also been found useful for the elicitation of requirements, the semantic sampling of existing databases, and the specification of schema mappings. This research establishes a toolbox of Armstrong databases for SQL data. This is challenging as SQL data can contain null marker occurrences in columns declared NULL, and may contain duplicate rows. Thus, the existing theory of Armstrong databases only applies to idealized instances of SQL data, that is, instances without null marker occurrences and without duplicate rows. For the thesis, two popular interpretations of null markers are considered: the no information interpretation used in SQL, and the exists but unknown interpretation by Codd. Furthermore, the study is limited to the popular class C of functional dependencies. However, the presence of duplicate rows means that the class of uniqueness constraints is no longer subsumed by the class of functional dependencies, in contrast to the relational model of data. As a first contribution a provably-correct algorithm is developed that computes Armstrong databases for an arbitrarily given finite set of uniqueness constraints and functional dependencies. This contribution is based on axiomatic, algorithmic and logical characterizations of the associated implication problem that are also established in this thesis. While the problem to decide whether a given database is Armstrong for a given set of such constraints is precisely exponential, our algorithm computes an Armstrong database with a number of rows that is at most quadratic in the number of rows of a minimum-sized Armstrong database. As a second contribution the algorithms are implemented in the form of a design tool. Users of the tool can therefore inspect Armstrong databases to analyze their current design choice Σ. Intuitively, Armstrong databases are useful for the acquisition of semantically meaningful constraints, if the users can recognize the actual meaningfulness of constraints that they incorrectly perceived as meaningless before the inspection of an Armstrong database. As a final contribution, measures are introduced that formalize the term “useful” and it is shown by some detailed experiments that Armstrong tables, as computed by the tool, are indeed useful. In summary, this research establishes a toolbox of Armstrong databases that can be applied by database designers to concisely visualize constraints on SQL data. Such support can lead to database designs that guarantee efficient data management in practice

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Engineering Systems Integration

Dreamers may envision our future, but it is the pragmatists who build it. Solve the right problem in the right way, mankind moves forward. Solve the right problem in the wrong way or the wrong problem in the right way, however clever or ingenious the solution, neither credits mankind. Instead, this misfire demonstrates a failure to appreciate a crucial step in pragmatic problem solving: systems integration. The first book to address the underlying premises of systems integration and how to exposit them in a practical and productive manner, Engineering Systems Integration: Theory, Metrics, and Methods looks at the fundamental nature of integration, exposes the subtle premises to achieve integration, and posits a substantial theoretical framework that is both simple and clear. Offering systems managers and systems engineers the framework from which to consider their decisions in light of systems integration metrics, the book isolates two basic questions, 1) Is there a way to express the interplay of human actions and the result of system interactions of a product with its environment?, and 2) Are there methods that combine to improve the integration of systems? The author applies the four axioms of General Systems Theory (holism, decomposition, isomorphism, and models) and explores the domains of history and interpretation to devise a theory of systems integration, develop practical guidance applying the three frameworks, and formulate the mathematical constructs needed for systems integration. The practicalities of integrating parts when we build or analyze systems mandate an analysis and evaluation of existing integrative frameworks of causality and knowledge. Integration is not just a word that describes a best practice, an art, or a single discipline. The act of integrating is an approach, operative in all disciplines, in all we see, in all we do

Engineering Systems Integration

Dreamers may envision our future, but it is the pragmatists who build it. Solve the right problem in the right way, mankind moves forward. Solve the right problem in the wrong way or the wrong problem in the right way, however clever or ingenious the solution, neither credits mankind. Instead, this misfire demonstrates a failure to appreciate a crucial step in pragmatic problem solving: systems integration. The first book to address the underlying premises of systems integration and how to exposit them in a practical and productive manner, Engineering Systems Integration: Theory, Metrics, and Methods looks at the fundamental nature of integration, exposes the subtle premises to achieve integration, and posits a substantial theoretical framework that is both simple and clear. Offering systems managers and systems engineers the framework from which to consider their decisions in light of systems integration metrics, the book isolates two basic questions, 1) Is there a way to express the interplay of human actions and the result of system interactions of a product with its environment?, and 2) Are there methods that combine to improve the integration of systems? The author applies the four axioms of General Systems Theory (holism, decomposition, isomorphism, and models) and explores the domains of history and interpretation to devise a theory of systems integration, develop practical guidance applying the three frameworks, and formulate the mathematical constructs needed for systems integration. The practicalities of integrating parts when we build or analyze systems mandate an analysis and evaluation of existing integrative frameworks of causality and knowledge. Integration is not just a word that describes a best practice, an art, or a single discipline. The act of integrating is an approach, operative in all disciplines, in all we see, in all we do

Strategic learning : a route to competitive advantage?

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Preferred customer status, supplier satisfaction and their contingencies

Over the last decades, firms shift increasingly from traditional in-house value creation strategies to cooperative buyer–supplier relationships as a source of value creation and competitive advantage. To reap extensive benefits from relationships with suppliers, assuring supplier satisfaction and achieving preferred customer status are key for buyers. In the past, several mechanisms have been identified that influence supplier satisfaction and preferred customer status; however, little is known about the role of major contingency factors such as: product type, dependencies and power influencing them. This dissertation focuses on the potential of preferred customer status to mitigate the negative effects of buyer dependency, the influences of a buyer’s power usage on supplier satisfaction, and potential perception differences of preferred status in buyer-supplier relationships. Additionally, this dissertation adds novel methods (i.e. partial least squares point predictions and polynomial regressions with response surface modeling) together with a dyadic perspective to supplier satisfaction and preferred customer research. These methods enable an assessment of the predictive abilities of models, reveal curvilinear interaction effects and discover asymmetric relationships between factors, which would have not been discovered otherwise

Development of a decision support system through modelling of critical infrastructure interdependencies : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Critical Infrastructure (CI) networks provide functional services to support the wellbeing of a community. Although it is possible to obtain detailed information about individual CI and their components, the interdependencies between different CI networks are often implicit, hidden or not well understood by experts. In the event of a hazard, failures of one or more CI networks and their components can disrupt the functionality and consequently affect the supply of services. Understanding the extent of disruption and quantification of the resulting consequences is important to assist various stakeholders' decision-making processes to complete their tasks successfully. A comprehensive review of the literature shows that a Decision Support System (DSS) integrated with appropriate modelling and simulation techniques is a useful tool for CI network providers and relevant emergency management personnel to understand the network recovery process of a region following a hazard event. However, the majority of existing DSSs focus on risk assessment or stakeholders' involvement without addressing the overall CI interdependency modelling process. Furthermore, these DSSs are primarily developed for data visualization or CI representation but not specifically to help decision-makers by providing them with a variety of customizable decision options that are practically viable. To address these limitations, a Knowledge-centred Decision Support System (KCDSS) has been developed in this study with the following aims: 1) To develop a computer-based DSS using efficient CI network recovery modelling algorithms, 2) To create a knowledge-base of various recovery options relevant to specific CI damage scenarios so that the decision-makers can test and verify several ‘what-if’ scenarios using a variety of control variables, and 3) To bridge the gap between hazard and socio-economic modelling tools through a multidisciplinary and integrated natural hazard impact assessment. Driven by the design science research strategy, this study proposes an integrated impact assessment framework using an iterative design process as its first research outcome. This framework has been developed as a conceptual artefact using a topology network-based approach by adopting the shortest path tree method. The second research outcome, a computer-based KCDSS, provides a convenient and efficient platform for enhanced decision making through a knowledge-base consisting of real-life recovery strategies. These strategies have been identified from the respective decision-makers of the CI network providers through the Critical Decision Method (CDM), a Cognitive Task Analysis (CTA) method for requirement elicitation. The capabilities of the KCDSS are demonstrated through electricity, potable water, and road networks in the Wellington region of Aotearoa New Zealand. The network performance has been analysed independently and with interdependencies to generate outage of services spatially and temporally. The outcomes of this study provide a range of theoretical and practical contributions. Firstly, the topology network-based analysis of CI interdependencies will allow a group of users to build different models, make and test assumptions, and try out different damage scenarios for CI network components. Secondly, the step-by-step process of knowledge elicitation, knowledge representation and knowledge modelling of CI network recovery tasks will provide a guideline for improved interactions between researchers and decision-makers in this field. Thirdly, the KCDSS can be used to test the variations in outage and restoration time estimates of CI networks due to the potential uncertainty related to the damage modelling of CI network components. The outcomes of this study also have significant practical implications by utilizing the KCDSS as an interface to integrate and add additional capabilities to the hazard and socio-economic modelling tools. Finally, the variety of ‘what-if’ scenarios embedded in the KCDSS would allow the CI network providers to identify vulnerabilities in their networks and to examine various post-disaster recovery options for CI reinstatement projects