Interactive visual analysis of location reporting patterns

Interactive visualization methods are often used to aid in the analysis of large datasets. We present a novel interactive visualization technique designed specifically for the analysis of location reporting patterns within large time-series datasets. We use a set of triangles with color coding to indicate the time between location reports. This allows reporting patterns (expected and unexpected) to be easily discerned during interactive analysis. We discuss the details of our method and describe evaluation both from expert opinion and from a user study.

How Evolutionary Visual Software Analytics Supports Knowledge Discovery

[EN] Evolutionary visual software analytics is a specialization of visual analytics. It is aimed at supporting software maintenance processes by aiding the understanding and comprehension of software evolution with the active participation of users. Therefore, it deals with the analysis of software projects that have been under development and maintenance for several years and which are usually formed by thousands of software artifacts,which are also associated to logs from communications, defect-tracking and software configuration management systems. Accordingly, evolutionary visual software analytics aims to assist software developers and software project managers by means of an integral approach that takes into account knowledge extraction techniques as well as visual representations that make use of interaction techniques and linked views. Consequently,this paper discusses the implementation of an architecture based on the evolutionary visual software analytics process and how it supports knowledge discovery during software maintenance tasks.[ES] Analítica de software visual evolutivos es una especialización de la analítica visual. Está dirigido a apoyar los procesos de mantenimiento de software, ayudando al entendimiento y la comprensión de la evolución del software, con la participación activa de los usuarios. Por lo tanto, tiene que ver con el análisis de los proyectos de software que han estado bajo desarrollo y mantenimiento por varios años y que por lo general están formados por miles de artefactos de software, que también están asociadas a los registros de las comunicaciones, seguimiento de defectos y sistemas de gestión de configuración de software. En consecuencia, la analítica de software visual evolutivos tiene como objetivo ayudar a los desarrolladores de software y administradores de proyectos de software a través de un enfoque integral que tenga en cuenta las técnicas de extracción de conocimiento, así como representaciones visuales que hacen uso de técnicas de interacción y vistas enlazadas. En consecuencia, en este documento se analiza la implementación de una arquitectura basada en el proceso de analítica de software visual evolutivos y la forma en que apoya el descubrimiento de conocimiento durante las tareas de mantenimiento de softwar

Interactive analysis of time intervals in a two-dimensional space

Time intervals are conventionally represented as linear segments in a one-dimensional space. An alternative representation of time intervals is the triangular model (TM), which represents time intervals as points in a two-dimensional space. In this paper, the use of TM in visualising and analysing time intervals is investigated. Not only does this model offer a compact visualisation of the distribution of intervals, it also supports an innovative temporal query mechanism that relies on geometries in the two-dimensional space. This query mechanism has the potential to simplify queries that are difficult to specify using traditional linear temporal query devices. Moreover, a software prototype that implements TM in a geographical information system (GIS) is introduced. This prototype has been applied in a real scenario to analyse time intervals that were detected by a Bluetooth tracking system. This application shows that TM has the potential to support a traditional GIS to analyse interval-based geographical data

On intelligible multimodal visual analysis

Analyzing data becomes an important skill in a more and more digital world. Yet, many users are facing knowledge barriers preventing them to independently conduct their data analysis. To tear down some of these barriers, multimodal interaction for visual analysis has been proposed. Multimodal interaction through speech and touch enables not only experts, but also novice users to effortlessly interact with such kind of technology. However, current approaches do not take the user differences into account. In fact, whether visual analysis is intelligible ultimately depends on the user. In order to close this research gap, this dissertation explores how multimodal visual analysis can be personalized. To do so, it takes a holistic view. First, an intelligible task space of visual analysis tasks is defined by considering personalization potentials. This task space provides an initial basis for understanding how effective personalization in visual analysis can be approached. Second, empirical analyses on speech commands in visual analysis as well as used visualizations from scientific publications further reveal patterns and structures. These behavior-indicated findings help to better understand expectations towards multimodal visual analysis. Third, a technical prototype is designed considering the previous findings. Enriching the visual analysis by a persistent dialogue and a transparency of the underlying computations, conducted user studies show not only advantages, but address the relevance of considering the user’s characteristics. Finally, both communications channels – visualizations and dialogue – are personalized. Leveraging linguistic theory and reinforcement learning, the results highlight a positive effect of adjusting to the user. Especially when the user’s knowledge is exceeded, personalizations helps to improve the user experience. Overall, this dissertations confirms not only the importance of considering the user’s characteristics in multimodal visual analysis, but also provides insights on how an intelligible analysis can be achieved. By understanding the use of input modalities, a system can focus only on the user’s needs. By understanding preferences on the output modalities, the system can better adapt to the user. Combining both directions imporves user experience and contributes towards an intelligible multimodal visual analysis

View-Dependent Visualization for Analysis of Large Datasets

Due to the impressive capabilities of human visual processing, interactive visualization methods have become essential tools for scientists to explore and analyze large, complex datasets. However, traditional approaches do not account for the increased size or latency of data retrieval when interacting with these often remote datasets. In this dissertation, I discuss two novel design paradigms, based on accepted models of the information visualization process and graphics hardware pipeline, that are appropriate for interactive visualization of large remote datasets. In particular, I discuss novel solutions aimed at improving the performance of interactive visualization systems when working with large numeric datasets and large terrain (elevation and imagery) datasets by using data reduction and asynchronous retrieval of view-prioritized data, respectively. First I present a modified version of the standard information visualization model that accounts for the challenges presented by interacting with large, remote datasets. I also provide the details of a software framework implemented using this model and discuss several different visualization applications developed within this framework. Next I present a novel technique for leveraging the hardware graphics pipeline to provide asynchronous, view-prioritized data retrieval to support interactive visualization of remote terrain data. I provide the results of statistical analysis of performance metrics to demonstrate the effectiveness of this approach. Finally I present the details of two novel visualization techniques, and the results of evaluating these systems using controlled user studies and expert evaluation. The results of these qualitative and quantitative evaluation mechanisms demonstrate improved visual analysis task performance for large numeric datasets

A method for the visualisation of historical multivariate spatial data

The use of spatial technologies and geographical concepts for historical investigation is an area of increasing academic interest. This interest has led to the emergence of the field of Historical Geographic Information Systems (Historical GIS), in which history and geography are inextricably linked in both technology and scholarship. Utilising historical sources requires greater emphasis to be placed on the temporal aspects of the data and often involves the tracking of historical events over time. This temporal focus has challenged the suitability of current visual representations utilised in Historical GIS. This is because the majority of projects rely on conventional geographic visualisation representation designed primarily for emphasising the geographic, not temporal, aspects of the data. This thesis establishes a visualisation method for investigating and analysing the temporal and geographical nature of historical events. In particular, it will focus on addressing the importance of visualising multiple variables and depicting change of these variables over time, with the aid of a Historical GIS and data graphics, for understanding the patterns and relationships between them. The visualisation method enables these temporal and geographic aspects of historical spatial data, along with multivariate capabilities, to be incorporated effectively by extending the spatial dimension of time-series graphs, meaningful classification, and by utilising the capacity to show change and variability in linear spatial objects. This thesis demonstrates the benefits gained from visualising historical, geographic, temporal, and multivariate data concurrently through a Case Study on the history of Melbourne’s cinema venues between 1946 and 1986. The history of cinema operation involves information on 289 cinema venues across a period of great cultural and technological change. Application of the visualisation method was used to address a number of Geographic Questions relating to specifics of cinema venue change by representing chosen venue locations and operation variables (such as seating capacity and ownership) over a specified temporal period. This has enabled data that is usually reserved for written communication to be visually analysed, interpreted, and displayed in a way not previously explored. It has been found that by creating visual access to multivariate and temporal spatial information it is possible to produce new insights into the geographic and temporal patterns and relationships present in historical data

Visual Analysis of In-Car Communication Networks

Analyzing, understanding and working with complex systems and large datasets has become a familiar challenge in the information era. The explosion of data worldwide affects nearly every part of society, particularly the science, engineering, health, and financial domains. Looking, for instance at the automotive industry, engineers are confronted with the enormously increased complexity of vehicle electronics. Over the years, a large number of advanced functions, such as ACC (adaptive cruise control), rear seat entertainment systems or automatic start/stop engines, has been integrated into the vehicle. Thereby, the functions have been more and more distributed over the vehicle, leading to the introduction of several communication networks. Overlooking all relevant data facets, understanding dependencies, analyzing the flow of messages and tracking down problems in these networks has become a major challenge for automotive engineers. Promising approaches to overcome information overload and to provide insight into complex data are Information Visualization (InfoVis) and Visual Analytics (VA). Over the last decades, these research communities spent much effort on developing new methods to help users obtain insight into complex data. However, few of these solutions have yet reached end users, and moving research into practice remains one of the great challenges in visual data analysis. This situation is particularly true for large company settings, where very little is known about additional challenges, obstacles and requirements in InfoVis/VA development and evaluation. Users have to be better integrated into our research processes in terms of adequate requirements analysis, understanding practices and challenges, developing well-directed, user-centered technologies and evaluating their value within a realistic context. This dissertation explores a novel InfoVis/VA application area, namely in-car communication networks, and demonstrates how information visualization methods and techniques can help engineers to work with and better understand these networks. Based on a three-year internship with a large automotive company and the close cooperation with domain experts, I grounded a profound understanding of specific challenges, requirements and obstacles for InfoVis/VA application in this area and learned that “designing with not for the people” is highly important for successful solutions. The three main contributions of this dissertation are: (1) An empirical analysis of current working practices of automotive engineers and the derivation of specific design requirements for InfoVis/VA tools; (2) the successful application and evaluation of nine prototypes, including the deployment of five systems; and (3) based on the three-year experience, a set of recommendations for developing and evaluating InfoVis systems in large company settings. I present ethnographic studies with more than 150 automotive engineers. These studies helped us to understand currently used tools, the underlying data, tasks as well as user groups and to categorize the field into application sub-domains. Based on these findings, we propose implications and recommendations for designing tools to support current practices of automotive network engineers with InfoVis/VA technologies. I also present nine InfoVis design studies that we built and evaluated with automotive domain experts and use them to systematically explore the design space of applying InfoVis to in-car communication networks. Each prototype was developed in a user-centered, participatory process, respectively with a focus on a specific sub-domain of target users with specific data and tasks. Experimental results from studies with real users are presented, that show that the visualization prototypes can improve the engineers’ work in terms of working efficiency, better understanding and novel insights. Based on lessons learned from repeatedly designing and evaluating our tools together with domain experts at a large automotive company, I discuss challenges and present recommendations for deploying and evaluating VA/InfoVis tools in large company settings. I hope that these recommendations can guide other InfoVis researchers and practitioners in similar projects by providing them with new insights, such as the necessity for close integration with current tools and given processes, distributed knowledge and high degree of specialization, and the importance of addressing prevailing mental models and time restrictions. In general, I think that large company settings are a promising and fruitful field for novel InfoVis applications and expect our recommendations to be useful tools for other researchers and tool designers