| dc.description.abstract | With the increasing capabilities of measurement devices and computing machines, the amount of
recorded data grows rapidly. It is so high that manual processing is no longer feasible.
The Visual Analytics approach is powerful because it combines the strengths of human recognition
and vision system with today’s computing power. Different, but strongly linked visualizations
and views provide unique perspectives on the same data elements. The views are linked using position
on the screen as well as color, which also plays a secondary role in indicating the degree
of similarity. This enables the human recognition system to identify trends and anomalies in a
network of measurement readings. As a result, the data analyst has the ability to approach more
complex questions such as: are there anomalies in the measurement records? What does the
network usually look like?
In this work we propose a collection of Visual Analytics approaches to support the user in exploratory
search and related tasks in graph data sets. One aspect is graph navigation, where we
use the information of existing labels to support the user in analyzing with this data set. Another
consideration is the preservation of the user’s mental map, which is supported by smooth transitions
between individual keyframes. The later chapters focus on sensor networks, a type of graph
data that additionally contains time series data on a per-node basis; this adds an extra dimension
of complexity to the problem space. This thesis contributes several techniques to the scientific
community in different domains and we summarize them as follows.
We begin with an approach for network exploration. This forms the basis for subsequent contributions,
as it to supports user in the orientation and the navigation in any kind of network structure.
This is achieved by providing a showing only a small subset of the data (in other words: a local
graph view). The user expresses interest in a certain area by selecting one of more focus nodes
that define the visible subgraph. Visual cues in the form of pointing arrows indicate other areas
of the graph that could be relevant for the user. Based on this network exploration paradigm, we
present a combination of different techniques that stabilize the layout of such local graph views
by reducing acting forces. As a result, the movement of nodes in the node-link diagram is reduced,
which reduces the mental effort to track changes on the screen. However, up to this point
the approach suffers from one of the most prominent shortcomings of force-directed graph layouts.
Little changes in the initial setup, force parameters, or graph topology changes have a strong
impact on the visual representation of the drawing. When the user explores the network, the set
of visible nodes continuously changes and therefore the layout will look different when an area
of the graph is visited a second time. This makes it difficult to identify differences or recognize
different drawing as equal in terms of topology. We contribute an approach for the deterministic
generation of layouts based on pre-computed layout patches that are stitched at runtime. This
ensures that even force-directed layouts are deterministic, allowing the analyst to recognize previously explored areas of the graph. In the next step, we apply these rather general purpose concepts
from theory in practical applications.
One of the most important network category is that of sensor networks, a type of graph data
structure where every node is annotated with a time series. Such networks exist in the form
of electric grids and other supply networks. In the wake of distributed and localized energy
generation, the analysis of these networks becomes more and more important. We present and
discuss a multi-view and multi-perspective environment for network analysis of sensor networks
that integrates different data sources. It is then extended into a visualization environment that
enables the analyst to track the automated analysis of the processing pipeline of an expert system.
As a result, the user can verify the correctness of the system and intervene where necessary. One
key issue with expert systems, which typically operate on manually written rules, is that they can
deal with explicit statements. They cannot grasp terms such as “uncommon” or “anomalous”.
Unfortunately, this is often what the domain experts are looking for. We therefore modify and
extend the system into an integrated analysis system for the detection of similar patterns in space
and in different granularities of time. Its purpose is to obtain an overview of a large system
and to identify hot spots and other anomalies. The idea here is to use similar colors to indicate
similar patterns in the network. For that, it is vital to be able to rely on the mapping of time
series patterns to color. The Colormap-Explorer supports the analysis and comparison of different
implementations of 2D color maps to find the best fit for the task.
As soon as the domain expert has identified problems in the networks, he or she might want
to take countermeasures to improve the network stability. We present an approach that integrates
simulation in the process to perform “What-If” analysis based on an underlying simulation framework.
Subsequent runs can be compared to quickly identify differences and discover the effect of
changes in the network.
The approaches that are presented can be utilized in a large variety of applications and application
domains. This enables the domain expert to navigate and explore networks, find key elements
such as bridges, and detect spurious trends early. | |
