Although prior research has compared modeling performance using different systems development methods, there has been little research examining the comprehensibility of models generated by those methods. in this paper...
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Although prior research has compared modeling performance using different systems development methods, there has been little research examining the comprehensibility of models generated by those methods. in this paper, we report the results of an empirical study comparing user comprehension of object-oriented (OO) and process-oriented (PO) models. The fundamental difference is that while OO models tend to focus on structure, PO models tend to emphasize behavior or processes. Proponents of the OO modeling approach argue that it lends itself naturally to the way humans think. However, evidence from research in cognitive psychology and human factors suggests that human problem solving is innately procedural. Given these conflicting viewpoints, we investigate empirically if OO models are in fact easier to understand than PO models. But, as suggested by the theory of cognitive fit, model comprehension may be influenced by task-specific characteristics. We, therefore, compare OO and PO models based on whether the comprehension activity involves: 1) only structural aspects, 2) only behavioral aspects, or 3) a combination of structural and behavioral aspects. We measure comprehension through subjects' responses to questions designed along these three dimensions. Two experiments were conducted, each with a different application and a different group of subjects. Each subject was first trained in both methods, and then participated in one of the two experiments, answering several questions relating to his or her comprehension of an OO or a PO model of a business application. The comprehension questions ranged in complexity from relatively simple (addressing either structural or behavioral aspects) to more complex ones (addressing both structural and behavioral aspects). Results show that for most of the simple questions, no significant difference was observed insofar as model comprehension is concerned. For most of the complex questions, however, the PO model was found to be
Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-sc...
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Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. Amodel-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be
The paper presents an attempt to conceptualize decision support and various generic subtasks, to develop a general architecture of intelligent decision support systems, and to exploit previous work on process-oriented...
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ISBN:
(纸本)9783030557881;9783030557898
The paper presents an attempt to conceptualize decision support and various generic subtasks, to develop a general architecture of intelligent decision support systems, and to exploit previous work on process-oriented diagnosis within this architecture. The primary subtasks whose (intelligent) solution is heavily dependent on domain knowledge are situation assessment, i.e. inferring what is happening in a system from a set of observations, and therapy proposal, i.e. developing plans for interventions to achieve certain goals starting from the current situation. Both tasks can be solved by an extension of consistency-based diagnosis to process-oriented models.
The paper presents an attempt to conceptualize decision support and various generic subtasks and to develop a general architecture of intelligent decision support systems. We decompose the task of decision support int...
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ISBN:
(纸本)9780987214317
The paper presents an attempt to conceptualize decision support and various generic subtasks and to develop a general architecture of intelligent decision support systems. We decompose the task of decision support into subtasks whose input, output, and function are characterized. This is based on a small number of concepts: besides "decision", the essential ones are "observation", "situation", "goal", "action", and "process", which are in turn defined using elementary concepts for characterizing the system under consideration, or our model thereof. This is not an academic exercise aiming at providing definitions, but a prerequisite for a generic architecture of decision support systems with interfaces for certain generic functions, the comparison of basic modules implementing these functions, and the configuration of systems from a set of such modules. The primary subtasks whose (intelligent) solution is heavily dependent on domain knowledge are situation assessment, i.e. inferring what is happening in a system from a set of observations, and therapy proposal, i.e. developing plans for interventions to achieve certain goals starting from the current situation. Secondary tasks are situation and plan evaluation (checking whether and to what extent a situation or plan satisfies or violates goals), prediction (forecasting the future development starting from a situation with or without interventions), and observation/experiment proposal (designing activities to collect information, possibly after stimulating the system in a particular way, useful to disambiguate situation assessment and also situation evaluation).
CAUSA is a knowledge acquisition tool which supports the incremental modeling of complex dynamic systems during the whole knowledge acquisition task. It provides an environment for the modeling and simulation of dynam...
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CAUSA is a knowledge acquisition tool which supports the incremental modeling of complex dynamic systems during the whole knowledge acquisition task. It provides an environment for the modeling and simulation of dynamic systems on a quantitative level. The environment provides a conceptual framework which includes primitives like objects, processes, and causal dependencies, allowing for the modeling of a broad class of complex systems. Simulation allows for the quantitative and qualitative inspection and empirical investigation of the behavior of the modeled system. CAUSA is implemented in Knowledge-Craft and runs on a Symbolics 3640.
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