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

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc

Conceptual modelling: A psychological perspective.

This thesis describes the formulation and experimental use of psychological principles that apply to conceptual modelling as practised during information systems development. The principles address cognition (perception, memory and mental models) and group dynamics. The aim is to determine whether application of fundamental psychological principles can help to make modellers, especially those who are relatively inexperienced, more effective. An experimental graphical modelling technique (method 'X') is presented that conforms to the psychological principles, together with a supporting software tool for visual construction of models in the design of typical business database systems. The effectiveness of both inexperienced and expert modellers using method 'X' in real business situations was compared with that of modellers using conventional object modelling. Data was gathered in a series of field experiments using participant observation, questionnaires, and interviews and by analysing the resulting models. With conventional object modelling, untrained modellers produced results that were grossly incomplete and incorrect (22-35%, on average). Using method 'X', untrained modellers produced models that were almost complete and correct (better than 82%). Significant productivity gains were observed with method 'X' (approximately 150% for expert modeller and over 450% for untrained modellers). For an expert modeller no measurable differences in quality were observed between methods, but the modeller regarded the quality of method 'X' models as better and expressed a preference method 'X' over the conventional approach. The results appear to support the idea of re-engineering conceptual modelling practice according to psychological first principles. The fact that more dramatic performance improvements were observed for inexperienced modellers suggests that modelling need not require a high degree of expertise, if methods and tools are adapted appropriately. The results could be exploited to empower untrained modellers, such as end users, who wish to develop large software systems but lack access to the skills of trained IT professionals