2 research outputs found
Inviwo -- A Visualization System with Usage Abstraction Levels
The complexity of today's visualization applications demands specific
visualization systems tailored for the development of these applications.
Frequently, such systems utilize levels of abstraction to improve the
application development process, for instance by providing a data flow network
editor. Unfortunately, these abstractions result in several issues, which need
to be circumvented through an abstraction-centered system design. Often, a high
level of abstraction hides low level details, which makes it difficult to
directly access the underlying computing platform, which would be important to
achieve an optimal performance. Therefore, we propose a layer structure
developed for modern and sustainable visualization systems allowing developers
to interact with all contained abstraction levels. We refer to this interaction
capabilities as usage abstraction levels, since we target application
developers with various levels of experience. We formulate the requirements for
such a system, derive the desired architecture, and present how the concepts
have been exemplary realized within the Inviwo visualization system.
Furthermore, we address several specific challenges that arise during the
realization of such a layered architecture, such as communication between
different computing platforms, performance centered encapsulation, as well as
layer-independent development by supporting cross layer documentation and
debugging capabilities
Visual Anomaly Detection in Event Sequence Data
Anomaly detection is a common analytical task that aims to identify rare
cases that differ from the typical cases that make up the majority of a
dataset. When applied to the analysis of event sequence data, the task of
anomaly detection can be complex because the sequential and temporal nature of
such data results in diverse definitions and flexible forms of anomalies. This,
in turn, increases the difficulty in interpreting detected anomalies. In this
paper, we propose an unsupervised anomaly detection algorithm based on
Variational AutoEncoders (VAE) to estimate underlying normal progressions for
each given sequence represented as occurrence probabilities of events along the
sequence progression. Events in violation of their occurrence probability are
identified as abnormal. We also introduce a visualization system, EventThread3,
to support interactive exploration and interpretations of anomalies within the
context of normal sequence progressions in the dataset through comprehensive
one-to-many sequence comparison. Finally, we quantitatively evaluate the
performance of our anomaly detection algorithm and demonstrate the
effectiveness of our system through a case study