4,774 research outputs found
Ontology based data warehousing for mining of heterogeneous and multidimensional data sources
Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
Interpretable statistics for complex modelling: quantile and topological learning
As the complexity of our data increased exponentially in the last decades, so has our
need for interpretable features. This thesis revolves around two paradigms to approach
this quest for insights.
In the first part we focus on parametric models, where the problem of interpretability
can be seen as a āparametrization selectionā. We introduce a quantile-centric
parametrization and we show the advantages of our proposal in the context of regression,
where it allows to bridge the gap between classical generalized linear (mixed)
models and increasingly popular quantile methods.
The second part of the thesis, concerned with topological learning, tackles the
problem from a non-parametric perspective. As topology can be thought of as a way
of characterizing data in terms of their connectivity structure, it allows to represent
complex and possibly high dimensional through few features, such as the number of
connected components, loops and voids. We illustrate how the emerging branch of
statistics devoted to recovering topological structures in the data, Topological Data
Analysis, can be exploited both for exploratory and inferential purposes with a special
emphasis on kernels that preserve the topological information in the data.
Finally, we show with an application how these two approaches can borrow strength
from one another in the identification and description of brain activity through fMRI
data from the ABIDE project
Cohort aggregation modelling for complex forest stands: Spruce-aspen mixtures in British Columbia
Mixed-species growth models are needed as a synthesis of ecological knowledge
and for guiding forest management. Individual-tree models have been commonly
used, but the difficulties of reliably scaling from the individual to the stand
level are often underestimated. Emergent properties and statistical issues
limit their effectiveness. A more holistic modelling of aggregates at the whole
stand level is a potentially attractive alternative. This work explores
methodology for developing biologically consistent dynamic mixture models where
the state is described by aggregate stand-level variables for species or
age/size cohorts. The methods are demonstrated and tested with a two-cohort
model for spruce-aspen mixtures named SAM. The models combine single-species
submodels and submodels for resource partitioning among the cohorts. The
partitioning allows for differences in competitive strength among species and
size classes, and for complementarity effects. Height growth reduction in
suppressed cohorts is also modelled. SAM fits well the available data, and
exhibits behaviors consistent with current ecological knowledge. The general
framework can be applied to any number of cohorts, and should be useful as a
basis for modelling other mixed-species or uneven-aged stands.Comment: Accepted manuscript, to appear in Ecological Modellin
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