Tim Lammarsch

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.

TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data

Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.

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.

Developing an Extended Task Framework for Exploratory Data Analysis Along the Structure of Time

Exploratory data analysis of time-oriented data is an important goal that Visual Analytics has to tackle. When users from real-world domains are asked about time-oriented tasks, they often refer to the unique structure of time (e.g., calendars, primitives, etc.). Several task frameworks have been developed, but none of them combines a complete, systematic approach with explicit attention to the structure of time. To fill this gap, we aim for complementing an established task framework with a rule set that explicitly models the structure of time for tasks. This rule set allows to consistently formulate tasks for evaluating time-oriented data analysis methods.

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.

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.

Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction

The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance‐based abstraction (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance‐based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance‐based cycle plot with Clevelands original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series.