Supporting management of sensor networks through interactive visual analysis

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 previ-