Date & Time : October 16 02:00 pm - 03:40 pm Location : Grand Ballroom B Chair : David Ebert Papers :
Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data
Authors: Bilal Alsallakh, Wolfgang Aigner, Silvia Miksch, Eduard Groller
Abstract : Contingency tables summarize the relations between categorical variables and
arise in both scientific and business domains. Asymmetrically large two-way
contingency tables pose a problem for common visualization methods. The
Contingency Wheel has been recently proposed as an interactive visual method
to explore and analyze such tables. However, the scalability and readability
of this method are limited when dealing with large and dense tables. In this
paper we present Contingency Wheel++, new visual analytics methods that
overcome these major shortcomings: (1) regarding automated methods, a measure
of association based on Pearson's residuals alleviates the bias of the raw
residuals originally used, (2) regarding visualization methods, a
frequency-based abstraction of the visual elements eliminates overlapping and
makes analyzing both positive and negative associations possible, and (3)
regarding the interactive exploration environment, a multi-level
overview+detail interface enables exploring individual data items that are
aggregated in the visualization or in the table using coordinated views. We
illustrate the applicability of these new methods with a use case and show
how they enable discovering and analyzing nontrivial patterns and
associations in large categorical data
Visual Classifier Training for Text Document Retrieval
Authors: Florian Heimerl, Steffen Koch, Harald Bosch, Thomas Ertl
Abstract : Performing exhaustive searches over a large number of text documents can be
tedious, since it is very hard to formulate search queries or define filter
criteria that capture an analyst's information need adequately.
Classification through machine learning has the potential to improve search
and filter tasks encompassing either complex or very specific information
needs, individually. Unfortunately, analysts who are knowledgeable in their
field are typically not machine learning specialists. Most classification
methods, however, require a certain expertise regarding their parametrization
to achieve good results. Supervised machine learning algorithms, in contrast,
rely on labeled data, which can be provided by analysts. However, the effort
for labeling can be very high, which shifts the problem from composing
complex queries or defining accurate filters to another laborious task, in
addition to the need for judging the trained classifier's quality. We
therefore compare three approaches for interactive classifier training in a
user study. All of the approaches are potential candidates for the
integration into a larger retrieval system. They incorporate active learning
to various degrees in order to reduce the labeling effort as well as to
increase effectiveness. Two of them encompass interactive visualization for
letting users explore the status of the classifier in context of the labeled
documents, as well as for judging the quality of the classifier in iterative
feedback loops. We see our work as a step towards introducing user controlled
classification methods in addition to text search and filtering for
increasing recall in analytics scenarios involving large corpora.
Relative N-Gram Signatures: Document Visualization at the Level of Character N-Grams
Abstract : The Common N-Gram (CNG) classifier is a text classification algorithm based
on the comparison of frequencies of character n-grams (strings of characters
of length n) that are the most common in the considered documents and classes
of documents. We present a text analytic visualization system that employs
the CNG approach for text classification and uses the differences in
frequency values of common n-grams in order to visually compare documents at
the sub-word level. The visualization method provides both an insight into
n-gram characteristics of documents or classes of documents and a visual
interpretation of the workings of the CNG classifier.
LeadLine: Interactive Visual Analysis of Text Data through Event Identification and Exploration
Abstract : Text data such as online news and microblogs bear valuable insights regarding
important events and responses to such events. Events are inherently
temporal, evolving over time. Existing visual text analysis systems have
provided temporal views of changes based on topical themes extracted from
text data. But few have associated topical themes with events that cause the
changes. In this paper, we propose an interactive visual analytics system,
LeadLine, to automatically identify meaningful events in news and social
media data and support exploration of the events. To characterize events,
LeadLine integrates topic modeling, event detection, and named entity
recognition techniques to automatically extract information regarding the
investigative 4 Ws: who, what, when, and where for each event. To further
support analysis of the text corpora through events, LeadLine allows users to
interactively examine meaningful events using the 4 Ws to develop an
understanding of how and why. Through representing large-scale text corpora
in the form of meaningful events, LeadLine provides a concise summary of the
corpora. LeadLine also supports the construction of simple narratives through
the exploration of events. To demonstrate the efficacy of LeadLine in
identifying events and supporting exploration, two case studies were
conducted using news and social media data.
The Deshredder: A Visual Analytic Approach to Reconstructing Shredded Documents
Authors: Patrick Butler, Prithwish Chakraborty, Naren Ramakrishnan
Abstract : Reconstruction of shredded documents remains a significant challenge.
Creating a better document reconstruction system enables not just recovery of
information accidentally lost but also understanding our limitations against
adversaries' attempts to gain access to information. Existing approaches to
reconstructing shredded documents adopt either a predominantly manual (e.g.,
crowd-sourcing) or a near automatic approach. We describe
\\textit{Deshredder}, a visual analytic approach that scales well and
effectively incorporates user input to direct the reconstruction
process.Deshredder represents shredded pieces as time series and uses nearest
neighbor matching techniques that enable matching both the contours of
shredded pieces as well as the content of shreds themselves. More
importantly, Deshredder's interface support visual analytics through user
interaction with similarity matrices as well as higher level assembly through
more complex stitching functions. We identify a functional task taxonomy
leading to design considerations for constructing deshredding solutions, and
describe how Deshredder applies to problems from the DARPA Shredder Challenge
through expert evaluations.
VAST Papers: Clustering, Classification, and Correlation
Session :
Clustering, Classification, and Correlation
Date & Time : October 16 10:30 am - 12:10 pm Location : Grand Ballroom B Chair : Daniel Weiskopf Papers :
A Correlative Analysis Process in a Visual Analytics Environment
Authors: Abish Malik, Ross Maciejewski, Yun Jang, Whitney Huang, Niklas Elmqvist, David Ebert
Abstract : Finding patterns and trends in spatial and temporal datasets has been a long
studied problem in statistics and different domains of science. This paper
presents a visual analytics approach for the interactive exploration and
analysis of spatiotemporal correlations among multivariate datasets. Our
approach enables users to discover correlations and explore potentially
causal or predictive links at different spatiotemporal aggregation levels
among the datasets, and allows them to understand the underlying statistical
foundations that precede the analysis. Our technique utilizes the Pearson's
product-moment correlation coefficient and factors in the lead or lag between
different datasets to detect trends and periodic patterns amongst them.
Scatter/Gather Clustering: Flexibly Incorporating User Feedback to Steer Clustering Results
Abstract : Significant effort has been devoted to designing clustering algorithms that
are responsive to user feedback or that incorporate prior domain knowledge in
the form of constraints. However, users desire more expressive forms of
interaction to influence clustering outcomes. In our experiences working with
diverse application scientists, we have identified an interaction style
scatter/gather clustering that helps users iteratively restructure clustering
results to meet their expectations. As the names indicate, scatter and gather
are dual primitives that describe whether clusters in a current segmentation
should be broken up further or, alternatively, brought back together. By
combining scatter and gather operations in a single step, we support very
expressive dynamic restructurings of data. Scatter/gather clustering is
implemented using a nonlinear optimization framework that achieves both
locality of clusters and satisfaction of user-supplied constraints. We
illustrate the use of our scatter/gather clustering approach in a visual
analytic application to study baffle shapes in the bat biosonar (ears and
nose) system. We demonstrate how domain experts are adept at supplying
scatter/gather constraints, and how our framework incorporates these
constraints effectively without requiring numerous instance-level
constraints.
Inter-Active Learning of Ad-Hoc Classifiers for Video Visual Analytics
Authors: Benjamin Hoferlin, Rudolf Netzel, Markus Hoferlin, Daniel Weiskopf, Gunther Heidemann
Abstract : Learning of classifiers to be used as filters within the analytical reasoning
process leads to new and aggravates existing challenges. Such classifiers are
typically trained ad-hoc, with tight time constraints that affect the amount
and the quality of annotation data and, thus, also the users' trust in the
classifier trained. We approach the challenges of ad-hoc training by
inter-active learning, which extends active learning by integrating human
experts' background knowledge to greater extent. In contrast to active
learning, not only does inter-active learning include the users' expertise by
posing queries of data instances for labeling, but it also supports the users
in comprehending the classifier model by visualization. Besides the
annotation of manually or automatically selected data instances, users are
empowered to directly adjust complex classifier models. Therefore, our model
visualization facilitates the detection and correction of inconsistencies
between the classifier model trained by examples and the user's mental model
of the class definition. Visual feedback of the training process helps the
users assess the performance of the classifier and, thus, build up trust in
the filter created. We demonstrate the capabilities of inter-active learning
in the domain of video visual analytics and compare its performance with the
results of random sampling and uncertainty sampling of training sets.
Visual Cluster Exploration of Web Clickstream Data
Authors: Jishang Wei, Zeqian Shen, Neel Sundaresan, Kwan-Liu Ma
Abstract : Web clickstream data are routinely collected to study how users browse the
web or use a service. It is clear that the ability to recognize and summarize
user behavior patterns from such data is valuable to e-commerce companies. In
this paper, we introduce a visual analytics system to explore the various
user behavior patterns reflected by distinct clickstream clusters. In a
practical analysis scenario, the system first presents an overview of
clickstream clusters using a Self-Organizing Map with Markov chain models.
Then the analyst can interactively explore the clusters through an intuitive
user interface. He can either obtain summarization of a selected group of
data or further refine the clustering result. We evaluated our system using
two different datasets from eBay. Analysts who were working on the same data
have confirmed the system's effectiveness in extracting user behavior
patterns from complex datasets and enhancing their ability to reason.
An Adaptive Parameter Space-Filling Algorithm for Highly Interactive Cluster Exploration
Authors: Zafar Ahmed, Chris Weaver
Abstract : For a user to perceive continuous interactive response time in a
visualization tool, the rule of thumb is that it must process, deliver, and
display rendered results for any given interaction in under 100 milliseconds.
In many visualization systems, successive interactions trigger independent
queries and caching of results. Consequently, computationally expensive
queries like multidimensional clustering cannot keep up with rapid sequences
of interactions, precluding visual benefits such as motion parallax. In this
paper, we describe a heuristic prefetching technique to improve the
interactive response time of KMeans clustering in dynamic query
visualizations of multidimensional data. We address the tradeoff between high
interaction and intense query computation by observing how related
interactions on overlapping data subsets produce similar clustering results,
and characterizing these similarities within a parameter space of
interaction. We focus on the two-dimensional parameter space defined by the
minimum and maximum values of a time range manipulated by dragging and
stretching a one-dimensional filtering lens over a plot of time series data.
Using calculation of nearest neighbors of interaction points in parameter
space, we reuse partial query results from prior interaction sequences to
calculate both an immediate best-effort clustering result and to schedule
calculation of an exact result. The method adapts to user interaction
patterns in the parameter space by reprioritizing the interaction neighbors
of visited points in the parameter space. A performance study on Mesonet
meteorological data demonstrates that the method is a significant improvement
over the baseline scheme in which interaction triggers on-demand, exact-range
clustering with LRU caching. We also present initial evidence that
approximate, temporary clustering results are sufficiently accurate (compared
to exact results) to convey useful cluster structure during rapid and
protracted interaction.
VAST Papers: Visual-Computational Analysis of Multivariate Data
Session :
Visual-Computational Analysis of Multivariate Data
Date & Time : October 16 04:15 pm - 05:55 pm Location : Grand Ballroom B Chair : Jean-Daniel Fekete Papers :
Authors: Eli T. Brown, Jingjing Liu, Carla E. Brodley, Remco Chang
Abstract : The world's corpora of data grow in size and complexity every day, making it
increasingly difficult for experts to make sense out of their data. Although
machine learning offers algorithms for finding patterns in data
automatically, they often require algorithm-specific parameters, such as an
appropriate distance function, which are outside the purview of a domain
expert. We present a system that allows an expert to interact directly with a
visual representation of the data to define an appropriate distance function,
thus avoiding direct manipulation of obtuse model parameters. Adopting an
iterative approach, our system first assumes a uniformly weighted Euclidean
distance function and projects the data into a two-dimensional scatterplot
view. The user can then move incorrectly-positioned data points to locations
that reflect his or her understanding of the similarity of those data points
relative to the other data points. Based on this input, the system performs
an optimization to learn a new distance function and then re-projects the
data to redraw the scatterplot. We illustrate empirically that with only a
few iterations of interaction and optimization, a user can achieve a
scatterplot view and its corresponding distance function that reflect the
user's knowledge of the data. In addition, we evaluate our system to assess
scalability in data size and data dimension, and show that our system is
computationally efficient and can provide an interactive or near-interactive
user experience.
Abstract : An essential element of exploratory data analysis is the use of revealing
low-dimensional projections of high-dimensional data. Projection Pursuit has
been an effective method for finding interesting low-dimensional projections
of multidimensional spaces by optimizing a score function called a projection
pursuit index. However, the technique is not scalable to high-dimensional
spaces. Here, we introduce a novel method for discovering noteworthy views of
high-dimensional data spaces by using binning and random projections. We
define score functions, akin to projection pursuit indices, that characterize
visual patterns of the low-dimensional projections that constitute feature
subspaces. We also describe an analytic, multivariate visualization platform
based on this algorithm that is scalable to extremely large problems.
iLAMP: Exploring High-Dimensional Spacing Through Backward Multidimensional Projection
Authors: Elisa Portes dos Santos Amorim, Emilio Vital Brazil, Joel Daniel II, Paulo Joia, Luis Gustavo Nonato
Abstract : Ever improving computing power and technological advances are greatly
augmenting data collection and scientific observation. This has directly
contributed to increased data complexity and dimensionality, motivating
research of exploration techniques for multidimensional data. Consequently, a
recent influx of work dedicated to techniques and tools that aid in
understanding multidimensional datasets can be observed in many research
fields, including biology, engineering, physics and scientific computing.
While the effectiveness of existing techniques to analyze the structure and
relationships of multidimensional data varies greatly, few techniques provide
flexible mechanisms to simultaneously visualize and actively explore
high-dimensional spaces. In this paper, we present an inverse linear affine
multidimensional projection, coined iLAMP, that enables a novel interactive
exploration technique for multidimensional data. iLAMP operates in reverse to
traditional projection methods by mapping low-dimensional information into a
high-dimensional space. This allows users to extrapolate instances of a
multidimensional dataset while exploring a projection of the data to the
planar domain. We present experimental results that validate iLAMP, measuring
the quality and coherence of the extrapolated data; as well as demonstrate
the utility of iLAMP to hypothesize the unexplored regions of a
high-dimensional space.
Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data
Authors: Andrada Tatu, Fabian Maas, Ines Farber, Enrico Bertini, Tobias Schreck, Thomas Seidl, Daniel Keim
Abstract : In explorative data analysis, the data under consideration often resides in a
high-dimensional (HD) data space. Currently many methods are available to
analyze this type of data. So far, proposed automatic approaches include
dimensionality reduction and cluster analysis, whereby visual-interactive
methods aim to provide effective visual mappings to show, relate, and
navigate HD data. Furthermore, almost all of these methods conduct the
analysis from a singular perspective, meaning that they consider the data in
either the original HD data space, or a reduced version thereof.
Additionally, HD data spaces often consist of combined features that measure
different properties, in which case the particular relationships between the
various properties may not be clear to the analysts a priori since it can
only be revealed if appropriate feature combinations (subspaces) of the data
are taken into consideration. Considering just a single subspace is, however,
often not sufficient since different subspaces may show complementary,
conjointly, or contradicting relations between data items. Useful information
may consequently remain embedded in sets of subspaces of a given HD input
data space. Relying on the notion of subspaces, we propose a novel method for
the visual analysis of HD data in which we employ an interestingness-guided
subspace search algorithm to detect a candidate set of subspaces. Based on
appropriately defined subspace similarity functions, we visualize the
subspaces and provide navigation facilities to interactively explore large
sets of subspaces. Our approach allows users to effectively compare and
relate subspaces with respect to involved dimensions and clusters of objects.
We apply our approach to synthetic and real data sets. We thereby demonstrate
its support for understanding HD data from different perspectives,
effectively yielding a more complete view on HD data.
Just-in-Time Annotation of Clusters, Outliers, and Trends in Point-based Data Visualizations
Authors: Eser Kandogan
Abstract : We introduce the concept of just-in-time descriptive analytics as a novel
application of computational and statistical techniques performed at
interaction-time to help users easily understand the structure of data as
seen in visualizations. Fundamental to just-in-time descriptive analytics is
(a) identifying visual features, such as clusters, outliers, and trends, user
might observe in visualizations automatically, (b) determining the semantics
of such features by performing statistical analysis as the user is
interacting, and (c) enriching visualizations with annotations that not only
describe semantics of visual features but also facilitate interaction to
support high-level understanding of data. In this paper, we demonstrate
just-in-time descriptive analytics applied to a point-based multi-dimensional
visualization technique to identify and describe clusters, outliers, and
trends. We argue that it provides a novel user experience of computational
techniques working alongside of users allowing them to build faster
qualitative mental models of data by demonstrating its application on a few
use-cases. Techniques used to facilitate just-in-time descriptive analytics
are described in detail along with their run-time performance
characteristics. We believe this is just a starting point and much remains to
be researched, as we discuss open issues and opportunities in improving
accessibility and collaboration.
VAST Papers: Sensemaking and Collaboration
Session :
Sensemaking and Collaboration
Date & Time : October 17 08:30 am - 10:10 am Location : Grand Ballroom B Chair : Niklas Elmqvist Papers :
Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts
Authors: Youn-ah Kang, John Stasko
Abstract : While the formal evaluation of systems in visual analytics is still
relatively uncommon, particularly rare are case studies of prolonged system
use by domain analysts working with their own data. Conducting case studies
can be challenging, but it can be a particularly effective way to examine
whether visual analytics systems are truly helping expert users to accomplish
their goals. We studied the use of a visual analytics system for sensemaking
tasks on documents by six analysts from a variety of domains. We describe
their application of the system along with the benefits, issues, and problems
that we uncovered. Findings from the studies identify features that visual
analytics systems should emphasize as well as missing capabilities that
should be addre ssed. These findings inform design implications for future
systems.
Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering
Authors: Alex Endert, Patrick Fiaux, Chris North
Abstract : Visual analytic tools aim to support the cognitively demanding task of
sensemaking. Their success often depends on the ability to leverage
capabilities of mathematical models, visualization, and human intuition
through flexible, usable, and expressive interactions. Spatially clustering
data is one effective metaphor for users to explore similarity and
relationships between information, adjusting the weighting of dimensions or
characteristics of the dataset to observe the change in the spatial layout.
Semantic interaction is an approach to user interaction in such
spatializations that couples these parametric modifications of the clustering
model with users' analytic operations on the data (e.g., direct document
movement in the spatialization, highlighting text, search, etc.). In this
paper, we present results of a user study exploring the ability of semantic
interaction in a visual analytic prototype, ForceSPIRE, to support
sensemaking. We found that semantic interaction captures the analytical
reasoning of the user through keyword weighting, and aids the user in
co-creating a spatialization based on the user's reasoning and intuition.
SocialNetSense: Supporting Sensemaking of Social and Structural Features in Networks with Interactive Visualization
Authors: Liang Gou, Xiaolong (Luke) Zhang, Airong Luo, Patricia F Anderson
Abstract : Increasingly, social network datasets contain social attribute information
about actors and their relationship. Analyzing such network with social
attributes requires making sense of not only its structural features, but
also the relationship between social features in attributes and network
structures. Existing social network analysis tools are usually weak in
supporting complex analytical tasks involving both structural and social
features, and often overlook users' needs for sensemaking tools that help to
gather, synthesize, and organize information of these features. To address
these challenges, we propose a sensemaking framework of social-network visual
analytics in this paper. This framework considers both bottom-up processes,
which are about constructing new understandings based on collected
information, and top-down processes, which concern using prior knowledge to
guide information collection, in analyzing social networks from both social
and structural perspectives. The framework also emphasizes the
externalization of sensemaking processes through interactive visualization.
Guided by the framework, we develop a system, SocialNetSense, to support the
sensemaking in visual analytics of social networks with social attributes.
The example of using our system to analyze a scholar collaboration network
shows that our approach can help users gain insight into social networks both
structurally and socially, and enhance their process awareness in visual
analytics.
An Affordance-Based Framework for Human Computation and Human-Computer Collaboration
Authors: R. Jordan Crouser, Remco Chang
Analyst's Workspace: An Embodied Sensemaking Environment for Large, High Resolution Displays
Authors: Christopher Andrews, Chris North
Abstract : Distributed cognition and embodiment provide compelling models for how humans
think and interact with the environment. Our examination of the use of large,
high-resolution displays from an embodied perspective has lead directly to
the development of a new sensemaking environment called Analyst's Workspace
(AW). AW leverages the embodied resources made more accessible through the
physical nature of the display to create a spatial workspace. By combining
spatial layout of documents and other artifacts with an entity-centric,
explorative investigative approach, AW aims to allow the analyst to
externalize elements of the sensemaking process as a part of the
investigation, integrated into the visual representations of the data itself.
In this paper, we describe the various capabilities of AW and discuss the key
principles and concepts underlying its design, emphasizing unique design
principles for designing visual analytic tools for large, high-resolution
displays.
VAST Papers: Applications, Design Studies and Tools
Session :
Applications, Design Studies and Tools
Date & Time : October 18 10:30 am - 12:10 pm Location : Grand Ballroom B Chair : Enrico Bertini Papers :
Visual Analytics Methodology for Eye Movement Studies
Authors: Gennady Andrienko, Natalia Andrienko, Michael Burch, Daniel Weiskopf
Abstract : Eye movement analysis is gaining popularity as a tool for evaluation of
visual displays and interfaces. However, the existing methods and tools for
analyzing eye movements and scanpaths are limited in terms of the tasks they
can support and effectiveness for large data and data with high variation. We
have performed an extensive empirical evaluation of a broad range of visual
analytics methods used in analysis of geographic movement data. The methods
have been tested for the applicability to eye tracking data and the
capability to extract useful knowledge about users' viewing behaviors. This
allowed us to select the suitable methods and match them to possible analysis
tasks they can support. The paper describes how the methods work in
application to eye tracking data and provides guidelines for method selection
depending on the analysis tasks
AlVis: Situation Awareness in the Surveillance of Road Tunnels
Authors: Harald Piringer, Matthias Buchetics, Rudolf Benedik
Abstract : In the surveillance of road tunnels, video data plays an important role for a
detailed inspection and as an input to systems for an automated detection of
incidents. In disaster scenarios like major accidents, however, the increased
amount of detected incidents may lead to situations where human operators
lose a sense of the overall meaning of that data, a problem commonly known as
a lack of situation awareness. The primary contribution of this paper is a
design study of AlVis, a system designed to increase situation awareness in
the surveillance of road tunnels. The design of AlVis is based on a
simplified tunnel model which enables an overview of the spatio-temporal
development of scenarios in real-time. The visualization explicitly
represents the present state, the history, and predictions of potential
future developments. Concepts for situation-sensitive prioritization of
information ensure scalability from normal operation to major disaster
scenarios. The visualization enables an intuitive access to live and historic
video for any point in time and space. We illustrate AlVis by means of a
scenario and report qualitative feedback by tunnel experts and operators.
This feedback suggests that AlVis is suitable to save time in recognizing
dangerous situations and helps to maintain an overview in complex disaster
scenarios.
Spatiotemporal Social Media Analytics for Abnormal Event Detection using Seasonal-Trend Decomposition
Authors: Junghoon Chae, Dennis Thom, Harald Bosch, Yun Jang, Ross Maciejewski, David S. Ebert, Thomas Ertl
Abstract : Recent advances in technology have enabled social media services to support
space-time indexed data, and internet users from all over the world have
created a large volume of time-stamped, geo-located data. Such spatiotemporal
data has immense value for increasing situational awareness of local events,
providing insights for investigations and understanding the extent of
incidents, their severity, and consequences, as well as their time-evolving
nature. In analyzing social media data, researchers have mainly focused on
finding temporal trends according to volume-based importance. Hence, a
relatively small volume of relevant messages may easily be obscured by a huge
data set indicating normal situations. In this paper, we present a visual
analytics approach that provides users with scalable and interactive social
media data analysis and visualization including the exploration and
examination of abnormal topics and events within various social media data
sources, such as Twitter, Flickr and YouTube. In order to find and understand
abnormal events, the analyst can first extract major topics from a set of
selected messages and rank them probabilistically using Latent Dirichlet
Allocation. He can then apply seasonal trend decomposition together with
traditional control chart methods to find unusual peaks and outliers within
topic time series. Our case studies show that situational awareness can be
improved by incorporating the anomaly and trend examination techniques into a
highly interactive visual analysis process.
Visual Analytics for the Big Data Era -- A Comparative Review of State-of-the-Art Commercial Systems
Authors: Leishi zhang, Andreas Stoffel, Michael Behrisch, Sebastian Mittelstadt, Tobias Schreck, Rene Pompl,
Smart Super Views - A Knowledge-Assisted Interface for Medical Visualization
Authors: Gabriel Mistelbauer, Hamed Bouzari, Rudiger Schernthaner, Ivan Baclija, Arnold Kochl, Stefan Bruckne
Abstract : Due to the ever growing volume of acquired data and information, users have
to be constantly aware of the methods for their exploration and for
interaction. Of these, not each might be applicable to the data at hand or
might reveal the desired result. Owing to this, innovations may be used
inappropriately and users may become skeptical. In this paper we propose a
knowledge-assisted interface for medical visualization, which reduces the
necessary effort to use new visualization methods, by providing only the most
relevant ones in a smart way. Consequently, we are able to expand such a
system with innovations without the users to worry about when, where, and
especially how they may or should use them. We present an application of our
system in the medical domain and give qualitative feedback from domain
experts.
VAST Papers: Space and Time, and The Analysis Process
Session :
Space and Time, and The Analysis Process
Date & Time : October 18 08:30 am - 10:10 am Location : Grand Ballroom B Chair : Bill Ribarsky Papers :
The User Puzzle - Explaining the Interaction with Visual Analytics Systems
Authors: Margit Pohl, Michael Smuc, Eva Mayr
Abstract : Visual analytics emphasizes the interplay between visualization, analytical
procedures performed by computers and human perceptual and cognitive
activities. Human reasoning is an important element in this context. There
are several theories in psychology and HCI explaining open-ended and
exploratory reasoning. Five of these theories (sensemaking theories, gestalt
theories, distributed cognition, graph comprehension theories and
skill-rule-knowledge models) are described in this paper. We discuss their
relevance for visual analytics. In order to do this more systematically, we
developed a schema of categories relevant for visual analytics research and
evaluation. All these theories have strengths but also weaknesses in
explaining interaction with visual analytics systems. A possibility to
overcome the weaknesses would be to combine two or more of these theories.
Enterprise Data Analysis and Visualization: An Interview Study
Authors: Sean Kandel, Andreas Paepcke, Joseph M. Hellerstein, Jeffrey Heer
Visual Analytics Methods for Categoric Spatio-Temporal Data
Authors: Tatiana von Landesberger, Sebastian Bremm, Natalia Andrienko, Gennady Andrienko, Maria Tekusova
Abstract : We focus on visual analysis of space- and time-referenced categorical data,
which describe possible states of spatial (geographical) objects or locations
and their changes over time. The analysis of these data is difficult as there
are only limited possibilities to analyze the three aspects (location, time
and category) simultaneously. We present a new approach which interactively
combines (a) visualization of categorical changes over time; (b) various
spatial data displays; (c) computational techniques for task-oriented
selection of time steps. They provide an expressive visualization with regard
to either the overall evolution over time or unusual changes. We apply our
approach on two use cases demonstrating its usefulness for a wide variety of
tasks. We analyze data from movement tracking and meteorologic areas. Using
our approach, expected events could be detected and new insights were gained.
A Visual Analytics Approach to Multi-scale Exploration of Environmental Time Series
Authors: Mike Sips, Patrick Kothur, Andrea Unger, Christian Hege, Doris Dransch
Abstract : We present a Visual Analytics approach that addresses the detection of
interesting patterns in numerical time series, specifically from
environmental sciences. Crucial for the detection of interesting temporal
patterns are the time scale and the starting points one is looking at. Our
approach makes no assumption about time scale and starting position of
temporal patterns and consists of three main steps: an algorithm to compute
statistical values for all possible time scales and starting positions of
intervals, visual identification of potentially interesting patterns in a
matrix visualization, and interactive exploration of detected patterns. We
demonstrate the utility of this approach in two scientific scenarios and
explain how it allowed scientists to gain new insight into the dynamics of
environmental systems.
Watch This: A Taxonomy for Dynamic Data Visualization
Authors: Joseph Cottam, Andrew Lumsdaine, Chris Weaver
Abstract : Visualizations embody design choices about data access, data transformation,
visual representation, and interaction. To interpret a static visualization,
a person must identify the correspondences between the visual representation
and the underlying data. These correspondences become moving targets when a
visualization is dynamic. Dynamics may be introduced in a visualization at
any point in the analysis and visualization process. For example, the data
itself may be streaming, shifting subsets may be selected, visual
representations may be animated, and interaction may modify presentation. In
this paper, we focus on the impact of dynamic data. We present a taxonomy and
conceptual framework for understanding how data changes influence the
interpretability of visual representations. Visualization techniques are
organized into categories at various levels of abstraction. The salient
characteristics of each category and task suitability are discussed through
examples from the scientific literature and popular practices. Examining the
implications of dynamically updating visualizations warrants attention
because it directly impacts the interpretability (and thus utility) of
visualizations. The taxonomy presented provides a reference point for further
exploration of dynamic data visualization techniques.
