14,034 research outputs found
Clear Visual Separation of Temporal Event Sequences
Extracting and visualizing informative insights from temporal event sequences
becomes increasingly difficult when data volume and variety increase. Besides
dealing with high event type cardinality and many distinct sequences, it can be
difficult to tell whether it is appropriate to combine multiple events into one
or utilize additional information about event attributes. Existing approaches
often make use of frequent sequential patterns extracted from the dataset,
however, these patterns are limited in terms of interpretability and utility.
In addition, it is difficult to assess the role of absolute and relative time
when using pattern mining techniques.
In this paper, we present methods that addresses these challenges by
automatically learning composite events which enables better aggregation of
multiple event sequences. By leveraging event sequence outcomes, we present
appropriate linked visualizations that allow domain experts to identify
critical flows, to assess validity and to understand the role of time.
Furthermore, we explore information gain and visual complexity metrics to
identify the most relevant visual patterns. We compare composite event learning
with two approaches for extracting event patterns using real world company
event data from an ongoing project with the Danish Business Authority.Comment: In Proceedings of the 3rd IEEE Symposium on Visualization in Data
Science (VDS), 201
Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem
High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or
Compositionally Complex Alloys (CCAs) are alloys that contain multiple
principal alloying elements. While many HEAs have been shown to have unique
properties, their discovery has been largely done through costly and
time-consuming trial-and-error approaches, with only an infinitesimally small
fraction of the entire possible composition space having been explored. In this
work, the exploration of the HEA composition space is framed as a Continuous
Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint
Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy
thermodynamic spaces. The algorithm is used to discover regions in the HEA
Composition-Temperature space that satisfy desired phase constitution
requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA
thermodynamic database. The database is first validated by comparing phase
stability predictions against experiments and then the CSA is deployed and
tested against design tasks consisting of identifying not only single phase
solid solution regions in ternary, quaternary and quinary composition spaces
but also the identification of regions that are likely to yield
precipitation-strengthened HEAs.Comment: 14 pages, 13 figure
Quantifying the interdisciplinarity of scientific journals and fields
There is an overall perception of increased interdisciplinarity in science,
but this is difficult to confirm quantitatively owing to the lack of adequate
methods to evaluate subjective phenomena. This is no different from the
difficulties in establishing quantitative relationships in human and social
sciences. In this paper we quantified the interdisciplinarity of scientific
journals and science fields by using an entropy measurement based on the
diversity of the subject categories of journals citing a specific journal. The
methodology consisted in building citation networks using the Journal Citation
Reports database, in which the nodes were journals and edges were established
based on citations among journals. The overall network for the 11-year period
(1999-2009) studied was small-world and scale free with regard to the
in-strength. Upon visualizing the network topology an overall structure of the
various science fields could be inferred, especially their interconnections. We
confirmed quantitatively that science fields are becoming increasingly
interdisciplinary, with the degree of interdisplinarity (i.e. entropy)
correlating strongly with the in-strength of journals and with the impact
factor.Comment: 23 pages, 6 figure
The Local Emergence and Global Diffusion of Research Technologies: An Exploration of Patterns of Network Formation
Grasping the fruits of "emerging technologies" is an objective of many
government priority programs in a knowledge-based and globalizing economy. We
use the publication records (in the Science Citation Index) of two emerging
technologies to study the mechanisms of diffusion in the case of two innovation
trajectories: small interference RNA (siRNA) and nano-crystalline solar cells
(NCSC). Methods for analyzing and visualizing geographical and cognitive
diffusion are specified as indicators of different dynamics. Geographical
diffusion is illustrated with overlays to Google Maps; cognitive diffusion is
mapped using an overlay to a map based on the ISI Subject Categories. The
evolving geographical networks show both preferential attachment and
small-world characteristics. The strength of preferential attachment decreases
over time, while the network evolves into an oligopolistic control structure
with small-world characteristics. The transition from disciplinary-oriented
("mode-1") to transfer-oriented ("mode-2") research is suggested as the crucial
difference in explaining the different rates of diffusion between siRNA and
NCSC
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
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