Alessio Bertone

Towards a conceptual framework for visual analytics of time and time-oriented data

Time is an important data dimension with distinct characteristics that is common across many application domains. This demands specialized methods in order to support proper analysis and visualization to explore trends, patterns, and relationships in different kinds of time-oriented data. The human perceptual system is highly sophisticated and specifically suited to spot visual patterns. For this reason, visualization is successfully applied in aiding these tasks. But facing the huge volumes of data to be analyzed today, applying purely visual techniques is often not sufficient. Visual analytics systems aim to bridge this gap by combining both, interactive visualization and computational analysis. In this paper, we introduce a concept for designing visual analytics frameworks and tailored visual analytics systems for time and time-oriented data. We present a number of relevant design choices and illustrate our concept by example.

Hierarchical Temporal Patterns and Interactive Aggregated Views for Pixel-Based Visualizations

Many real-world problems involve time-oriented data. Time data is different from other kinds of data--explicitly harnessing the structures of time in visualizations can guide and support users’ visual analysis processes. State-of-the-art visualizations hardly take advantage of the structures of time to aid users in understanding and exploring the data. To bring more flexibility to the analysis process, we have developed interactive visual methods incorporating the structures of time within a pixel-based visualization called GROOVE (granular overview overlay). GROOVE uses different techniques to visualize time-oriented data by overlaying several time granularities in one visualization and provides interactive operators, which utilize the structures of time in different ways to capture and explore time-oriented data.

A Comparison of Programming Platforms for Interactive Visualization in Web Browser Based Applications

Recently, Web browser based applications have become very popular in many domains. However, the specific requirements of interactive Information Visualization (InfoVis) applications in terms of graphics performance and interactivity have not yet been investigated systematically in this context. In order to assess browser-based application platforms, we provide a systematic comparison of server-based rendering, Java Applets, Flash, and Silverlight from several points of view. We aim to aid InfoVis developers in choosing the appropriate technology for their needs.

Visualizations at First Sight: Do Insights Require Training?

Understanding novel visualizations can be a challenge even forexperienced users. During iterative usability engineering phases inthe DisCō project, visualizations of time-oriented data areexplored by domain experts and non-experts. The aim of our study isto analyze the generation of knowledge and understanding by meansof visualizations without previous user training. Focusing onapplicability in various business domains for personnel planningand time scheduling, we tested mockups of visualizations with amethod based on user-reported insights. Results show almostidentical behavior of domain experts and non-experts whengenerating insights into the data from scratch. In the course ofworking with a visualization, an interchange of insights into thevisualization and insights into the data was found.

Special Section on Visual Analytics: Mind the time: Unleashing temporal aspects in pattern discovery

Temporal Data Mining is a core concept of Knowledge Discovery in Databases handling time-oriented data. State-of-the-art methods are capable of preserving the temporal order of events as well as the temporal intervals in between. The temporal characteristics of the events themselves, however, can likely lead to numerous uninteresting patterns found by current approaches. We present a new definition of the temporal characteristics of events and enhance related work for pattern finding by utilizing temporal relations, like meets, starts, or during, instead of just intervals between events. These prerequisites result in MEMuRY, a new procedure for Temporal Data Mining that preserves and mines additional time-oriented information. Our procedure is supported by SAPPERLOT, an interactive visual interface for exploring the patterns. Furthermore, we illustrate the efficiency of our procedure presenting a benchmark of the procedures run-time behavior. A usage scenario shows how the procedure can provide new insights.

Introduction to the special issue on visual analytics and knowledge discovery

The papers in this Special Issue present the state of the art in Visual Analytics and Knowledge Discovery and Data Mining (KDD), as well as propose potential extensions and research questions to further advance and integrate these two fields.The papers in this Special Issue present the state of the art in Visual Analytics and Knowledge Discovery and Data Mining (KDD), as well as propose potential extensions and research questions to fu...

Tutorial: introduction to visual analytics

Visual Analytics is an emerging area of research and practice that aims for supporting analytical reasoning by interactive visual interfaces. The basic idea is the integration of the outstanding capabilities of humans in terms of visual information exploration and the enormous processing power of computers to form a powerful knowledge discovery environment. In the course of our half-day tutorial we will introduce this multi-disciplinary field by discussing its key issues of analytical reasoning, perception & cognition, visualization interaction, computation mining, the visual analysis process, and show potential application areas.

Comparing Information Visualization Tools Focusing on the Temporal Dimensions

Empirical comparisons and categorizations of information visualization tools lack important considerations: the former undervalue the need for a theoretical background, and the latter tend to have too much distance from the user because they do not consider definite user tasks. Therefore, our work combines these approaches and presents the results of both a qualitative evaluation and a recently published categorization. We focus on the visualization of temporal data and reveal that current tools realize only a small part of the visualization possibilities in this field.

Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

Data Mining on time-oriented data has many real-world applications, like optimizing shift plans for shops or hospitals, or analyzing traffic or climate. As those data are often very large and multi-variate, several methods for symbolic representation of time-series have been proposed. Some of them are statistically robust, have a lower-bound distance measure, and are easy to configure, but do not consider temporal structures and domain knowledge of users. Other approaches, proposed as basis for Apriori pattern finding and similar algorithms, are strongly configurable, but the parametrization is hard to perform, resulting in ad-hoc decisions. Our contribution combines the strengths of both approaches: an interactive visual interface that helps defining event classes by applying statistical computations and domain knowledge at the same time. We are not focused on a particular application domain, but intend to make our approach useful for any kind of time-oriented data.

Does Jason Bourne need Visual Analytics to catch the Jackal

Visual Analytics is a relatively new field which tries to combine and intertwine visual and analytical methods in an interactive manner. Because of the complex structure of time, the application of visual analytics methods to timeoriented data is a very promising approach for insight generation. To show how this can be applied, on top of real world data we created a fictitious scenario where even one of Ludlum’s heroes, Jason Bourne, could take advantage of the collaboration between visual and analytical methods.