4,503 research outputs found
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis
Exploring data requires a fast feedback loop from the analyst to the system,
with a latency below about 10 seconds because of human cognitive limitations.
When data becomes large or analysis becomes complex, sequential computations
can no longer be completed in a few seconds and data exploration is severely
hampered. This article describes a novel computation paradigm called
Progressive Computation for Data Analysis or more concisely Progressive
Analytics, that brings at the programming language level a low-latency
guarantee by performing computations in a progressive fashion. Moving this
progressive computation at the language level relieves the programmer of
exploratory data analysis systems from implementing the whole analytics
pipeline in a progressive way from scratch, streamlining the implementation of
scalable exploratory data analysis systems. This article describes the new
paradigm through a prototype implementation called ProgressiVis, and explains
the requirements it implies through examples.Comment: 10 page
Uncertainty-aware video visual analytics of tracked moving objects
Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009
Informatics: the fuel for pharmacometric analysis
The current informal practice of pharmacometrics as a combination art and science makes it hard to appreciate the role that informatics can and should play in the future of the discipline and to comprehend the gaps that exist because of its absence. The development of pharmacometric informatics has important implications for expediting decision making and for improving the reliability of decisions made in model-based development. We argue that well-defined informatics for pharmacometrics can lead to much needed improvements in the efficiency, effectiveness, and reliability of the pharmacometrics process.
The purpose of this paper is to provide a description of the pervasive yet often poorly appreciated role of informatics in improving the process of data assembly, a critical task in the delivery of pharmacometric analysis results. First, we provide a brief description of the pharmacometric analysis process. Second, we describe the business processes required to create analysis-ready data sets for the pharmacometrician.
Third, we describe selected informatic elements required to support the pharmacometrics and data assembly processes. Finally, we offer specific suggestions for performing a systematic analysis of existing challenges as an approach to defi ning the next generation of pharmacometric informatics
An Entropy Search Portfolio for Bayesian Optimization
Bayesian optimization is a sample-efficient method for black-box global
optimization. How- ever, the performance of a Bayesian optimization method very
much depends on its exploration strategy, i.e. the choice of acquisition
function, and it is not clear a priori which choice will result in superior
performance. While portfolio methods provide an effective, principled way of
combining a collection of acquisition functions, they are often based on
measures of past performance which can be misleading. To address this issue, we
introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio
construction which is motivated by information theoretic considerations. We
show that ESP outperforms existing portfolio methods on several real and
synthetic problems, including geostatistical datasets and simulated control
tasks. We not only show that ESP is able to offer performance as good as the
best, but unknown, acquisition function, but surprisingly it often gives better
performance. Finally, over a wide range of conditions we find that ESP is
robust to the inclusion of poor acquisition functions.Comment: 10 pages, 5 figure
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
While the demand for machine learning (ML) applications is booming, there is
a scarcity of data scientists capable of building such models. Automatic
machine learning (AutoML) approaches have been proposed that help with this
problem by synthesizing end-to-end ML data processing pipelines. However, these
follow a best-effort approach and a user in the loop is necessary to curate and
refine the derived pipelines. Since domain experts often have little or no
expertise in machine learning, easy-to-use interactive interfaces that guide
them throughout the model building process are necessary. In this paper, we
present Visus, a system designed to support the model building process and
curation of ML data processing pipelines generated by AutoML systems. We
describe the framework used to ground our design choices and a usage scenario
enabled by Visus. Finally, we discuss the feedback received in user testing
sessions with domain experts.Comment: Accepted for publication in the 2019 Workshop on Human-In-the-Loop
Data Analytics (HILDA'19), co-located with SIGMOD 201
J-PET Framework: Software platform for PET tomography data reconstruction and analysis
J-PET Framework is an open-source software platform for data analysis,
written in C++ and based on the ROOT package. It provides a common environment
for implementation of reconstruction, calibration and filtering procedures, as
well as for user-level analyses of Positron Emission Tomography data. The
library contains a set of building blocks that can be combined by users with
even little programming experience, into chains of processing tasks through a
convenient, simple and well-documented API. The generic input-output interface
allows processing the data from various sources: low-level data from the
tomography acquisition system or from diagnostic setups such as digital
oscilloscopes, as well as high-level tomography structures e.g. sinograms or a
list of lines-of-response. Moreover, the environment can be interfaced with
Monte Carlo simulation packages such as GEANT and GATE, which are commonly used
in the medical scientific community.Comment: 14 pages, 5 figure
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