366,113 research outputs found

    Investigating Differences between Graphical and Textual Declarative Process Models

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    Declarative approaches to business process modeling are regarded as well suited for highly volatile environments, as they enable a high degree of flexibility. However, problems in understanding declarative process models often impede their adoption. Particularly, a study revealed that aspects that are present in both imperative and declarative process modeling languages at a graphical level-while having different semantics-cause considerable troubles. In this work we investigate whether a notation that does not contain graphical lookalikes, i.e., a textual notation, can help to avoid this problem. Even though a textual representation does not suffer from lookalikes, in our empirical study it performed worse in terms of error rate, duration and mental effort, as the textual representation forces the reader to mentally merge the textual information. Likewise, subjects themselves expressed that the graphical representation is easier to understand

    Graphical workstation capability for reliability modeling

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    In addition to computational capabilities, software tools for estimating the reliability of fault-tolerant digital computer systems must also provide a means of interfacing with the user. Described here is the new graphical interface capability of the hybrid automated reliability predictor (HARP), a software package that implements advanced reliability modeling techniques. The graphics oriented (GO) module provides the user with a graphical language for modeling system failure modes through the selection of various fault-tree gates, including sequence-dependency gates, or by a Markov chain. By using this graphical input language, a fault tree becomes a convenient notation for describing a system. In accounting for any sequence dependencies, HARP converts the fault-tree notation to a complex stochastic process that is reduced to a Markov chain, which it can then solve for system reliability. The graphics capability is available for use on an IBM-compatible PC, a Sun, and a VAX workstation. The GO module is written in the C programming language and uses the graphical kernal system (GKS) standard for graphics implementation. The PC, VAX, and Sun versions of the HARP GO module are currently in beta-testing stages

    On network modeling of manufacturing and related processes using GERT Summary report

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    Network modeling of manufacturing and related processes using Graphical Evaluation and Review Techniqu

    Inversion-based control of electromechanical systems using causal graphical descriptions

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    Causal Ordering Graph and Energetic Macroscopic Representation are graphical descriptions to model electromechanical systems using integral causality. Inversion rules have been defined in order to deduce control structure step-bystep from these graphical descriptions. These two modeling tools can be used together to develop a two-layer control of system with complex parts. A double-drive paper system is taken as an example. The deduced control yields good performances of tension regulation and velocity tracking

    Inversion-based control of electromechanical systems using causal graphical descriptions

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    Causal Ordering Graph and Energetic Macroscopic Representation are graphical descriptions to model electromechanical systems using integral causality. Inversion rules have been defined in order to deduce control structure step-bystep from these graphical descriptions. These two modeling tools can be used together to develop a two-layer control of system with complex parts. A double-drive paper system is taken as an example. The deduced control yields good performances of tension regulation and velocity tracking

    Sparse Nested Markov models with Log-linear Parameters

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    Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which captures equality constraints found in marginals of directed acyclic graph (DAG) models. Some of these constraints, such as the so called `Verma constraint', strictly generalize conditional independence. To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model. Placing such limits is similar in spirit to sparsity methods for undirected graphical models, and regression models. In this paper, we give a log-linear parameterization which allows sparse modeling with nested Markov models. We illustrate the advantages of this parameterization with a simulation study.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013
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