Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

Advances in software, embedded computing, sensors, and networking technologies will lead to a new generation of smart cyber-physical systems that will far exceed the capabilities of today’s embedded systems. They will be entrusted with increasingly complex tasks like controlling electric grids or autonomously driving cars. These systems have the potential to lay the foundations for tomorrow’s critical infrastructures, to form the basis of emerging and future smart services, and to improve the quality of our everyday lives in many areas. In order to solve their tasks, they have to continuously monitor and collect data from physical processes, analyse this data, and make decisions based on it. Making smart decisions requires a deep understanding of the environment, internal state, and the impacts of actions. Such deep understanding relies on efficient data models to organise the sensed data and on advanced analytics. Considering that cyber-physical systems are controlling physical processes, decisions need to be taken very fast. This makes it necessary to analyse data in live, as opposed to conventional batch analytics. However, the complex nature combined with the massive amount of data generated by such systems impose fundamental challenges. While data in the context of cyber-physical systems has some similar characteristics as big data, it holds a particular complexity. This complexity results from the complicated physical phenomena described by this data, which makes it difficult to extract a model able to explain such data and its various multi-layered relationships. Existing solutions fail to provide sustainable mechanisms to analyse such data in live. This dissertation presents a novel approach, named model-driven live analytics. The main contribution of this thesis is a multi-dimensional graph data model that brings raw data, domain knowledge, and machine learning together in a single model, which can drive live analytic processes. This model is continuously updated with the sensed data and can be leveraged by live analytic processes to support decision-making of cyber-physical systems. The presented approach has been developed in collaboration with an industrial partner and, in form of a prototype, applied to the domain of smart grids. The addressed challenges are derived from this collaboration as a response to shortcomings in the current state of the art. More specifically, this dissertation provides solutions for the following challenges: First, data handled by cyber-physical systems is usually dynamic—data in motion as opposed to traditional data at rest—and changes frequently and at different paces. Analysing such data is challenging since data models usually can only represent a snapshot of a system at one specific point in time. A common approach consists in a discretisation, which regularly samples and stores such snapshots at specific timestamps to keep track of the history. Continuously changing data is then represented as a finite sequence of such snapshots. Such data representations would be very inefficient to analyse, since it would require to mine the snapshots, extract a relevant dataset, and finally analyse it. For this problem, this thesis presents a temporal graph data model and storage system, which consider time as a first-class property. A time-relative navigation concept enables to analyse frequently changing data very efficiently. Secondly, making sustainable decisions requires to anticipate what impacts certain actions would have. Considering complex cyber-physical systems, it can come to situations where hundreds or thousands of such hypothetical actions must be explored before a solid decision can be made. Every action leads to an independent alternative from where a set of other actions can be applied and so forth. Finding the sequence of actions that leads to the desired alternative, requires to efficiently create, represent, and analyse many different alternatives. Given that every alternative has its own history, this creates a very high combinatorial complexity of alternatives and histories, which is hard to analyse. To tackle this problem, this dissertation introduces a multi-dimensional graph data model (as an extension of the temporal graph data model) that enables to efficiently represent, store, and analyse many different alternatives in live. Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks these systems typically need to share context information between computational entities. This requires analytic algorithms to reason over distributed data, which is a complex task since it relies on the aggregation and processing of various distributed and constantly changing data. To address this challenge, this dissertation proposes an approach to transparently distribute the presented multi-dimensional graph data model in a peer-to-peer manner and defines a stream processing concept to efficiently handle frequent changes. Fourthly, to meet future needs, cyber-physical systems need to become increasingly intelligent. To make smart decisions, these systems have to continuously refine behavioural models that are known at design time, with what can only be learned from live data. Machine learning algorithms can help to solve this unknown behaviour by extracting commonalities over massive datasets. Nevertheless, searching a coarse-grained common behaviour model can be very inaccurate for cyber-physical systems, which are composed of completely different entities with very different behaviour. For these systems, fine-grained learning can be significantly more accurate. However, modelling, structuring, and synchronising many fine-grained learning units is challenging. To tackle this, this thesis presents an approach to define reusable, chainable, and independently computable fine-grained learning units, which can be modelled together with and on the same level as domain data. This allows to weave machine learning directly into the presented multi-dimensional graph data model. In summary, this thesis provides an efficient multi-dimensional graph data model to enable live analytics of complex, frequently changing, and distributed data of cyber-physical systems. This model can significantly improve data analytics for such systems and empower cyber-physical systems to make smart decisions in live. The presented solutions combine and extend methods from model-driven engineering, [email protected], data analytics, database systems, and machine learning

An Uncertainty Visual Analytics Framework for Functional Magnetic Resonance Imaging

Improving understanding of the human brain is one of the leading pursuits of modern scientific research. Functional magnetic resonance imaging (fMRI) is a foundational technique for advanced analysis and exploration of the human brain. The modality scans the brain in a series of temporal frames which provide an indication of the brain activity either at rest or during a task. The images can be used to study the workings of the brain, leading to the development of an understanding of healthy brain function, as well as characterising diseases such as schizophrenia and bipolar disorder. Extracting meaning from fMRI relies on an analysis pipeline which can be broadly categorised into three phases: (i) data acquisition and image processing; (ii) image analysis; and (iii) visualisation and human interpretation. The modality and analysis pipeline, however, are hampered by a range of uncertainties which can greatly impact the study of the brain function. Each phase contains a set of required and optional steps, containing inherent limitations and complex parameter selection. These aspects lead to the uncertainty that impacts the outcome of studies. Moreover, the uncertainties that arise early in the pipeline, are compounded by decisions and limitations further along in the process. While a large amount of research has been undertaken to examine the limitations and variable parameter selection, statistical approaches designed to address the uncertainty have not managed to mitigate the issues. Visual analytics, meanwhile, is a research domain which seeks to combine advanced visual interfaces with specialised interaction and automated statistical processing designed to exploit human expertise and understanding. Uncertainty visual analytics (UVA) tools, which aim to minimise and mitigate uncertainties, have been proposed for a variety of data, including astronomical, financial, weather and crime. Importantly, UVA approaches have also seen success in medical imaging and analysis. However, there are many challenges surrounding the application of UVA to each research domain. Principally, these involve understanding what the uncertainties are and the possible effects so they may be connected to visualisation and interaction approaches. With fMRI, the breadth of uncertainty arising in multiple stages along the pipeline and the compound effects, make it challenging to propose UVAs which meaningfully integrate into pipeline. In this thesis, we seek to address this challenge by proposing a unified UVA framework for fMRI. To do so, we first examine the state-of-the-art landscape of fMRI uncertainties, including the compound effects, and explore how they are currently addressed. This forms the basis of a field we term fMRI-UVA. We then present our overall framework, which is designed to meet the requirements of fMRI visual analysis, while also providing an indication and understanding of the effects of uncertainties on the data. Our framework consists of components designed for the spatial, temporal and processed imaging data. Alongside the framework, we propose two visual extensions which can be used as standalone UVA applications or be integrated into the framework. Finally, we describe a conceptual algorithmic approach which incorporates more data into an existing measure used in the fMRI analysis pipeline

Abstract visualization of large-scale time-varying data

The explosion of large-scale time-varying datasets has created critical challenges for scientists to study and digest. One core problem for visualization is to develop effective approaches that can be used to study various data features and temporal relationships among large-scale time-varying datasets. In this dissertation, we first present two abstract visualization approaches to visualizing and analyzing time-varying datasets. The first approach visualizes time-varying datasets with succinct lines to represent temporal relationships of the datasets. A time line visualizes time steps as points and temporal sequence as a line. They are generated by sampling the distributions of virtual words across time to study temporal features. The key idea of time line is to encode various data properties with virtual words. We apply virtual words to characterize feature points and use their distribution statistics to measure temporal relationships. The second approach is ensemble visualization, which provides a highly abstract platform for visualizing an ensemble of datasets. Both approaches can be used for exploration, analysis, and demonstration purposes. The second component of this dissertation is an animated visualization approach to study dramatic temporal changes. Animation has been widely used to show trends, dynamic features and transitions in scientific simulations, while animated visualization is new. We present an automatic animation generation approach that simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. We also extend the concept of animated visualization to non-traditional time-varying datasets--network protocols--for visualizing key information in abstract sequences. We have evaluated the effectiveness of our animated visualization with a formal user study and demonstrated the advantages of animated visualization for studying time-varying datasets