18,686 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
Requirements for building information modeling based lean production management systems for construction
Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last
Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face
BioSimulator.jl: Stochastic simulation in Julia
Biological systems with intertwined feedback loops pose a challenge to
mathematical modeling efforts. Moreover, rare events, such as mutation and
extinction, complicate system dynamics. Stochastic simulation algorithms are
useful in generating time-evolution trajectories for these systems because they
can adequately capture the influence of random fluctuations and quantify rare
events. We present a simple and flexible package, BioSimulator.jl, for
implementing the Gillespie algorithm, -leaping, and related stochastic
simulation algorithms. The objective of this work is to provide scientists
across domains with fast, user-friendly simulation tools. We used the
high-performance programming language Julia because of its emphasis on
scientific computing. Our software package implements a suite of stochastic
simulation algorithms based on Markov chain theory. We provide the ability to
(a) diagram Petri Nets describing interactions, (b) plot average trajectories
and attached standard deviations of each participating species over time, and
(c) generate frequency distributions of each species at a specified time.
BioSimulator.jl's interface allows users to build models programmatically
within Julia. A model is then passed to the simulate routine to generate
simulation data. The built-in tools allow one to visualize results and compute
summary statistics. Our examples highlight the broad applicability of our
software to systems of varying complexity from ecology, systems biology,
chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages
the use of stochastic simulation, minimizes tedious programming efforts, and
reduces errors during model specification.Comment: 27 pages, 5 figures, 3 table
Advancing Critical Care in the ICU: A Human-Centered Biomedical Data Visualization Systems
The purpose of this research is to provide medical clinicians with a new technology for interpreting large and diverse datasets to expedite critical care decision-making in the ICU. We refer to this technology as the medical information visualization assistant (MIVA). MIVA delivers multivariate biometric (bedside) data via a visualization display by transforming and organizing it into temporal resolutions that can provide contextual knowledge to clinicians. The result is a spatial organization of multiple datasets that allows rapid analysis and interpretation of trends. Findings from the usability study of the MIVA static prototype and heuristic inspection of the dynamic prototype suggest that using MIVA can yield faster and more accurate results. Furthermore, comments from the majority of the experimental group and the heuristic inspectors indicate that MIVA can facilitate clinical task flow in context-dependent health care settings
Utilizing Software Analytics to Guide Software Development
Modern software systems often produce vast amounts of software usage data. Previous work, however, has indicated that such data is often left unutilized. This leaves a gap for methods and practices that put the data to use.
The objective of this thesis is to determine and test concrete methods for utilizing software usage data and to learn what use cases and benefits can be achieved via such methods.
The study consists of two interconnected parts. Firstly, a semi-structured literature review is conducted to identify methods and use cases for software usage data. Secondly, a subset of the identified methods is experimented with by conducting a case study to determine how developers and managers experience the methods.
We found that there exists a wide range of methods for utilizing software usage data. Via these methods, a wide range of software development-related use cases can be fulfilled. However, in practice, apart from debugging purposes, software usage data is largely left unutilized. Furthermore, developers and managers share a positive attitude towards employing methods of utilizing software usage data.
In conclusion, software usage data has a lot of potential. Besides, developers and managers are interested in putting software usage data utilization methods to use. Furthermore, the information available via these methods is difficult to replace. In other words, methods for utilizing software usage data can provide irreplaceable information that is relevant and useful for both managers and developers. Therefore, practitioners should consider introducing methods for utilizing software usage data in their development practices
Visual analytics for supply network management: system design and evaluation
We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip
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