366,113 research outputs found
Investigating Differences between Graphical and Textual Declarative Process Models
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
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
Network modeling of manufacturing and related processes using Graphical Evaluation and Review Techniqu
Inversion-based control of electromechanical systems using causal graphical descriptions
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
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
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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