16 research outputs found
Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs
Annual recruitment data of new graduates are manually analyzed by human
resources specialists (HR) in industries, which signifies the need to evaluate
the recruitment strategy of HR specialists. Every year, different applicants
send in job applications to companies. The relationships between applicants'
attributes (e.g., English skill or academic credential) can be used to analyze
the changes in recruitment trends across multiple years' data. However, most
attributes are unnormalized and thus require thorough preprocessing. Such
unnormalized data hinder the effective comparison of the relationship between
applicants in the early stage of data analysis. Thus, a visual exploration
system is highly needed to gain insight from the overview of the relationship
between applicants across multiple years. In this study, we propose the
Polarizing Attributes for Network Analysis of Correlation on Entities
Association (Panacea) visualization system. The proposed system integrates a
time-varying graph model and dynamic graph visualization for heterogeneous
tabular data. Using this system, human resource specialists can interactively
inspect the relationships between two attributes of prospective employees
across multiple years. Further, we demonstrate the usability of Panacea with
representative examples for finding hidden trends in real-world datasets and
then describe HR specialists' feedback obtained throughout Panacea's
development. The proposed Panacea system enables HR specialists to visually
explore the annual recruitment of new graduates
Multivariate Pointwise Information-Driven Data Sampling and Visualization
With increasing computing capabilities of modern supercomputers, the size of
the data generated from the scientific simulations is growing rapidly. As a
result, application scientists need effective data summarization techniques
that can reduce large-scale multivariate spatiotemporal data sets while
preserving the important data properties so that the reduced data can answer
domain-specific queries involving multiple variables with sufficient accuracy.
While analyzing complex scientific events, domain experts often analyze and
visualize two or more variables together to obtain a better understanding of
the characteristics of the data features. Therefore, data summarization
techniques are required to analyze multi-variable relationships in detail and
then perform data reduction such that the important features involving multiple
variables are preserved in the reduced data. To achieve this, in this work, we
propose a data sub-sampling algorithm for performing statistical data
summarization that leverages pointwise information theoretic measures to
quantify the statistical association of data points considering multiple
variables and generates a sub-sampled data that preserves the statistical
association among multi-variables. Using such reduced sampled data, we show
that multivariate feature query and analysis can be done effectively. The
efficacy of the proposed multivariate association driven sampling algorithm is
presented by applying it on several scientific data sets.Comment: 25 page
Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda
While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues
Angular-based Edge Bundled Parallel Coordinates Plot for the Visual Analysis of Large Ensemble Simulation Data
With the continuous increase in the computational power and resources of
modern high-performance computing (HPC) systems, large-scale ensemble
simulations have become widely used in various fields of science and
engineering, and especially in meteorological and climate science. It is widely
known that the simulation outputs are large time-varying, multivariate, and
multivalued datasets which pose a particular challenge to the visualization and
analysis tasks. In this work, we focused on the widely used Parallel
Coordinates Plot (PCP) to analyze the interrelations between different
parameters, such as variables, among the members. However, PCP may suffer from
visual cluttering and drawing performance with the increase on the data size to
be analyzed, that is, the number of polylines. To overcome this problem, we
present an extension to the PCP by adding B\'{e}zier curves connecting the
angular distribution plots representing the mean and variance of the
inclination of the line segments between parallel axes. The proposed
Angular-based Parallel Coordinates Plot (APCP) is capable of presenting a
simplified overview of the entire ensemble data set while maintaining the
correlation information between the adjacent variables. To verify its
effectiveness, we developed a visual analytics prototype system and evaluated
by using a meteorological ensemble simulation output from the supercomputer
Fugaku
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the
time-frequency distribution of radio signals and analyzing their time-varying
patterns of communication states in radio monitoring and management. While it
excels when performing short-term signal analyses, it becomes inadaptable for
long-term signal analyses because it cannot adequately depict signal
time-varying patterns in a large time span on a space-limited screen. This
research thus presents an abstract signal time-frequency (ASTF) diagram to
address this problem. In the diagram design, a visual abstraction method is
proposed to visually encode signal communication state changes in time slices.
A time segmentation algorithm is proposed to divide a large time span into time
slices.Three new quantified metrics and a loss function are defined to ensure
the preservation of important time-varying information in the time
segmentation. An algorithm performance experiment and a user study are
conducted to evaluate the effectiveness of the diagram for long-term signal
analyses.Comment: 11 pages, 9 figure
Checking Data Quality of Longitudinal Household Travel Survey Data
Ensuring data quality of household travel survey data is often tedious and, thus, time-consuming. To speed up the process of data-checking and to gain an in-depth understanding of the data, data visualization is a practical, fundamental tool. Since 1994, data visualization has been used in the German Mobility Panel (MOP) data-checking process. This paper presents two graphical visualization tools developed for the MOP. Both tools speed up the data checks and ensure high consistency in identifying erroneous data. This paper describes and discusses how the tools provide a continuous data quality assessment
Expert evaluation of the usability of HeloVis: a 3D Immersive Helical Visualization for SIGINT Analysis
International audienceThis paper presents an evaluation of HeloVis: a 3D interactive visualization that relies on immersive properties to improve user performance during SIGnal INTelligence (SIGINT) analysis. HeloVis draws on perceptive biases, highlighted by Gestalt laws, and on depth perception to enhance the recurrence properties contained in the data. In this paper, we briefly recall what is SIGINT, the challenges that it brings to visual analytics, and the limitations of state of the art SIGINT tools. Then, we present HeloVis, and we evaluate its efficiency through the results of an evaluation that we have made with civil and military operators who are the expert end-users of SIGINT analysis
Community detection applied on big linked data
The Linked Open Data (LOD) Cloud has more than tripled its sources in just six years (from 295 sources in 2011 to 1163 datasets in 2017). The actual Web of Data contains more then 150 Billions of triples. We are assisting at a staggering growth in the production and consumption of LOD and the generation of increasingly large datasets. In this scenario, providing researchers, domain experts, but also businessmen and citizens with visual representations and intuitive interactions can significantly aid the exploration and understanding of the domains and knowledge represented by Linked Data. Various tools and web applications have been developed to enable the navigation, and browsing of the Web of Data. However, these tools lack in producing high level representations for large datasets, and in supporting users in the exploration and querying of these big sources. Following this trend, we devised a new method and a tool called H-BOLD (High level visualizations on Big Open Linked Data). H-BOLD enables the exploratory search and multilevel analysis of Linked Open Data. It offers different levels of abstraction on Big Linked Data. Through the user interaction and the dynamic adaptation of the graph representing the dataset, it will be possible to perform an effective exploration of the dataset, starting from a set of few classes and adding new ones. Performance and portability of H-BOLD have been evaluated on the SPARQL endpoint listed on SPARQL ENDPOINT STATUS. The effectiveness of H-BOLD as a visualization tool is described through a user study
From complex data to clear insights: visualizing molecular dynamics trajectories
Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization
A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews
Autonomous multi-robot systems, where a team of robots shares information to
perform tasks that are beyond an individual robot's abilities, hold great
promise for a number of applications, such as planetary exploration missions.
Each robot in a multi-robot system that uses the shared-world coordination
paradigm autonomously schedules which robot should perform a given task, and
when, using its worldview--the robot's internal representation of its belief
about both its own state, and other robots' states. A key problem for operators
is that robots' worldviews can fall out of sync (often due to weak
communication links), leading to desynchronization of the robots' scheduling
decisions and inconsistent emergent behavior (e.g., tasks not performed, or
performed by multiple robots). Operators face the time-consuming and difficult
task of making sense of the robots' scheduling decisions, detecting
de-synchronizations, and pinpointing the cause by comparing every robot's
worldview. To address these challenges, we introduce MOSAIC Viewer, a visual
analytics system that helps operators (i) make sense of the robots' schedules
and (ii) detect and conduct a root cause analysis of the robots' desynchronized
worldviews. Over a year-long partnership with roboticists at the NASA Jet
Propulsion Laboratory, we conduct a formative study to identify the necessary
system design requirements and a qualitative evaluation with 12 roboticists. We
find that MOSAIC Viewer is faster- and easier-to-use than the users' current
approaches, and it allows them to stitch low-level details to formulate a
high-level understanding of the robots' schedules and detect and pinpoint the
cause of the desynchronized worldviews.Comment: To appear in IEEE Conference on Visual Analytics Science and
Technology (VAST) 202