48,598 research outputs found
Health Figures: An Open Source JavaScript Library for Health Data Visualization
The way we look at data has a great impact on how we can understand it,
particularly when the data is related to health and wellness. Due to the
increased use of self-tracking devices and the ongoing shift towards preventive
medicine, better understanding of our health data is an important part of
improving the general welfare of the citizens. Electronic Health Records,
self-tracking devices and mobile applications provide a rich variety of data
but it often becomes difficult to understand. We implemented the hFigures
library inspired on the hGraph visualization with additional improvements. The
purpose of the library is to provide a visual representation of the evolution
of health measurements in a complete and useful manner. We researched the
usefulness and usability of the library by building an application for health
data visualization in a health coaching program. We performed a user evaluation
with Heuristic Evaluation, Controlled User Testing and Usability
Questionnaires. In the Heuristics Evaluation the average response was 6.3 out
of 7 points and the Cognitive Walkthrough done by usability experts indicated
no design or mismatch errors. In the CSUQ usability test the system obtained an
average score of 6.13 out of 7, and in the ASQ usability test the overall
satisfaction score was 6.64 out of 7. We developed hFigures, an open source
library for visualizing a complete, accurate and normalized graphical
representation of health data. The idea is based on the concept of the hGraph
but it provides additional key features, including a comparison of multiple
health measurements over time. We conducted a usability evaluation of the
library as a key component of an application for health and wellness
monitoring. The results indicate that the data visualization library was
helpful in assisting users in understanding health data and its evolution over
time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016
Simple and Effective Visual Models for Gene Expression Cancer Diagnostics
In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple two-dimensional plots such as scatterplot and radviz graph. The principal innovation proposed in the paper is a method called VizRank, which is able to score and identify the best among possibly millions of candidate projections for visualizations. Compared to recently much applied techniques in the field of cancer genomics that include neural networks, support vector machines and various ensemble-based approaches, VizRank is fast and finds visualization models that can be easily examined and interpreted by domain experts. Our experiments on a number of gene expression data sets show that VizRank was always able to find data visualizations with a small number of (two to seven) genes and excellent class separation. In addition to providing grounds for gene expression cancer diagnosis, VizRank and its visualizations also identify small sets of relevant genes, uncover interesting gene interactions and point to outliers and potential misclassifications in cancer data sets
VizRank: Data Visualization Guided by Machine Learning
Data visualization plays a crucial role in identifying interesting patterns in exploratory data analysis. Its use is, however, made difficult by the large number of possible data projections showing different attribute subsets that must be evaluated by the data analyst. In this paper, we introduce a method called VizRank, which is applied on classified data to automatically select the most useful data projections. VizRank can be used with any visualization method that maps attribute values to points in a two-dimensional visualization space. It assesses possible data projections and ranks them by their ability to visually discriminate between classes. The quality of class separation is estimated by computing the predictive accuracy of k-nearest neighbor classifier on the data set consisting of x and y positions of the projected data points and their class information. The paper introduces the method and presents experimental results which show that VizRank's ranking of projections highly agrees with subjective rankings by data analysts. The practical use of VizRank is also demonstrated by an application in the field of functional genomics
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
BINet: Multi-perspective Business Process Anomaly Classification
In this paper, we introduce BINet, a neural network architecture for
real-time multi-perspective anomaly detection in business process event logs.
BINet is designed to handle both the control flow and the data perspective of a
business process. Additionally, we propose a set of heuristics for setting the
threshold of an anomaly detection algorithm automatically. We demonstrate that
BINet can be used to detect anomalies in event logs not only on a case level
but also on event attribute level. Finally, we demonstrate that a simple set of
rules can be used to utilize the output of BINet for anomaly classification. We
compare BINet to eight other state-of-the-art anomaly detection algorithms and
evaluate their performance on an elaborate data corpus of 29 synthetic and 15
real-life event logs. BINet outperforms all other methods both on the synthetic
as well as on the real-life datasets
GraphCombEx: A Software Tool for Exploration of Combinatorial Optimisation Properties of Large Graphs
We present a prototype of a software tool for exploration of multiple
combinatorial optimisation problems in large real-world and synthetic complex
networks. Our tool, called GraphCombEx (an acronym of Graph Combinatorial
Explorer), provides a unified framework for scalable computation and
presentation of high-quality suboptimal solutions and bounds for a number of
widely studied combinatorial optimisation problems. Efficient representation
and applicability to large-scale graphs and complex networks are particularly
considered in its design. The problems currently supported include maximum
clique, graph colouring, maximum independent set, minimum vertex clique
covering, minimum dominating set, as well as the longest simple cycle problem.
Suboptimal solutions and intervals for optimal objective values are estimated
using scalable heuristics. The tool is designed with extensibility in mind,
with the view of further problems and both new fast and high-performance
heuristics to be added in the future. GraphCombEx has already been successfully
used as a support tool in a number of recent research studies using
combinatorial optimisation to analyse complex networks, indicating its promise
as a research software tool
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