160,254 research outputs found
Business Process Retrieval Based on Behavioral Semantics
This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie
GaGa: A parsimonious and flexible model for differential expression analysis
Hierarchical models are a powerful tool for high-throughput data with a small
to moderate number of replicates, as they allow sharing information across
units of information, for example, genes. We propose two such models and show
its increased sensitivity in microarray differential expression applications.
We build on the gamma--gamma hierarchical model introduced by Kendziorski et
al. [Statist. Med. 22 (2003) 3899--3914] and Newton et al. [Biostatistics 5
(2004) 155--176], by addressing important limitations that may have hampered
its performance and its more widespread use. The models parsimoniously describe
the expression of thousands of genes with a small number of hyper-parameters.
This makes them easy to interpret and analytically tractable. The first model
is a simple extension that improves the fit substantially with almost no
increase in complexity. We propose a second extension that uses a mixture of
gamma distributions to further improve the fit, at the expense of increased
computational burden. We derive several approximations that significantly
reduce the computational cost. We find that our models outperform the original
formulation of the model, as well as some other popular methods for
differential expression analysis. The improved performance is specially
noticeable for the small sample sizes commonly encountered in high-throughput
experiments. Our methods are implemented in the freely available Bioconductor
gaga package.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS244 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System
In an intelligent tutoring system (ITS), the domain expert should provide\ud
relevant domain knowledge to the tutor so that it will be able to guide the\ud
learner during problem solving. However, in several domains, this knowledge is\ud
not predetermined and should be captured or learned from expert users as well as\ud
intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud
techniques can help to build this domain intelligence in ITS. This paper proposes\ud
a framework to capture problem-solving knowledge using a promising approach\ud
of data and knowledge discovery based on a combination of sequential pattern\ud
mining and association rules discovery techniques. The framework has been implemented\ud
and is used to discover new meta knowledge and rules in a given domain\ud
which then extend domain knowledge and serve as problem space allowing\ud
the intelligent tutoring system to guide learners in problem-solving situations.\ud
Preliminary experiments have been conducted using the framework as an alternative\ud
to a path-planning problem solver in CanadarmTutor
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