International Workshop on Visual Analytics (2012) K. Matkovic and G. Santucci (Editors) Developing an Extended Task Framework for Exploratory Data Analysis Along the Structure of Time T. Lammarsch, A. Rind, W. Aigner, and S. Miksch Institute of Software Technology and Interactive Systems (ISIS), Vienna University of Technology, Austria Abstract 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 comple- menting 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. Categories and Subject Descriptors (according to ACM CCS): Information Systems [H.1.1]: Models and Principles—Systems and Information Theory; Computing Methodologies [I.m]: Miscellaneous— 1. Introduction Human judgement plays a fundamental role in Visual An- alytics (VA) and is primarily mediated through interactive visual interfaces [TC05]. Therefore, it is necessary to take into account the users and be aware of their goals and men- tal models. For exploratory data analysis (EDA) of time- oriented data, they usually consider the structure of time, for example the aspect of calendric systems (see Section 3). Smuc et al. [SML * 09] present detailed examples resulting from an insight study: “Starting in the morning, it rises to a peak around 10 or 11 a.m. It then calms down by noon, but there is a second peak around 4 or 5 p.m., after which it decreases again.” “The first Monday is high, the second is lower, but it rises again on the third and fourth.” The authors organize these insights using a bottom-up, and also a top-down approach, but both are spread around spe- cific examples, even if they try to generalize from there. Thus, they cannot make a statement about the completeness of the insights or explain for which kinds of insights a tool is suitable [SML * 09]. Existing task frameworks, like the one by Andrienko and Andrienko [AA06] approach this problem by starting at the most general and abstract level, where it is possible to define a complete set of tasks. For example, they phrase tasks like “look for the characteristics at a given ref- erence” and provide a formal rule set that describes these. They do provide details in the form of illustrative example cases, and only those are formulated according to the aspects of the structure of time. However, these example cases do not cover the design space completely, and the rules account for the unique characteristics of time only implicitly. Thus, there is a gap between the complete formal a-priori defini- tion of tasks, for example performed in the Andrienko and Andrienko [AA06] task framework (AATF), and tasks lists that stem from free exploration, for example shown by Smuc et al. [SML * 09] or arbitrary consideration by task develop- ers. To evaluate an application in a top-down approach, or to evaluate the completeness of insights in a bottom-up ap- proach, a task taxonomy for the dataset used is necessary. The structure of time imposes a number of aspects on such taxonomies that are always the same. The actual tasks con- tain a subset of them. We phrase these aspects for fitting them into the AATF, which is used because it is formally complete but also extendable (see Section 2). We have to adapt the aspects so that they fit into the framework’s formal- ism. The result is a rule set that explains how to phrase tasks in a way that pays heed to the specific characteristics of time- oriented data. Hence, a main contribution of our work is a task framework that guides the development of test cases. 2. Related Work Many task frameworks exist in the visualization and HCI communities. Most of them are concerned with low-level c  The Eurographics Association 2012.