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Supporting management of sensor networks through interactive visual analysis

: Steiger, Martin
: Fellner, Dieter W.; Kohlhammer, Jörn

Volltext ()

Darmstadt, 2015, 216 S.
Darmstadt, TU, Diss., 2015
URN: urn:nbn:de:tuda-tuprints-46517
Dissertation, Elektronische Publikation
Fraunhofer IGD ()
Business Field: Visual decision support; Research Area: Human computer interaction (HCI); graph drawing; Graphical User Interface (GUI); time series analysis; time series data visualization; decision support

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 previvously 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.