Proceedings of the 2007 Winter Simulation Conference S. G. Henderson, B. Biller, M.-H. Hsieh, J. Shortle, J. D. Tew, and R. R. Barton, eds. TOWARDS A CONCEPTUAL FRAMEWORK FOR VISUAL ANALYTICS OF TIME AND TIME-ORIENTED DATA Wolfgang Aigner Alessio Bertone Silvia Miksch Department of Information and Knowledge Engineering Dr.-Karl-Dorrek-Strasse 30, Danube University Krems A-3500 Krems, AUSTRIA Christian Tominski Heidrun Schumann Department of Computer Science Albert-Einstein-Strasse 21, University of Rostock D-18059 Rostock, GERMANY ABSTRACT Time is an important data dimension with distinct charac- teristics 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. 1 MOTIVATION AND BACKGROUND During the last decade, capabilities to both generate and collect data have seen an explosive growth. Advances in scientific and business data collection (e.g., from remote sensors, from space satellites, or from retail and production devices as well as growingly complex simulation systems) have generated a flood of data and information. Advances in data storage technology such as faster and cheaper storage devices with higher capacity, better database management systems, and data warehousing technology have allowed us to transform this data into “mountains” of stored data. Such volumes of data and information overwhelm most traditional manual methods of data analysis such as spread- sheets, ad-hoc queries, or simple visualizations. The need for new methods and tools which can intelligently and (semi-)automatically transform data into information and furthermore, synthesize knowledge are a core area of the emerging field of Visual Analytics. The basic idea of Visual Analytics (Thomas and Cook 2005) 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. Both visual as well as analytical methods are combined intertwinedly to fully support this process. Most importantly, the user is not merely a passive element who interprets the outcome of visual and analytical methods but she is the core entity who drives the whole process. Time is an important data dimension that is common across many application domains, like transport, call cen- ters, retail, production, health care, police, or financial services as well as for research in medicine, biology, and economics. Particularly, in the area of simulation systems, time is central to simulating dynamic system behavior as reflected by Robinson in his definition of computer-based dynamic simulation as being “an imitation (on a computer) of a system as it progresses through time” (Robinson 2004). Exploring trends, patterns, and relationships are particularly important tasks when dealing with time-oriented data and information. In contrast to other quantitative data dimen- sions that are usually “flat”, time has an inherent semantic structure which increases its complexity dramatically. The hierarchical structure of granularities in time, as for exam- ple minutes, hours, days, weeks, months, is unlike most other quantitative dimensions. Specifically, time comprises different forms of divisions (e.g., 60 minutes resemble one hour while 24 hours resemble one day) and granularities are combined to form calendar systems (e.g., Gregorian, Julian, Business, or Academic calendars). Moreover, time contains natural cycles and re-occurrences, as for example seasons, but also social–somehow irregular–cycles, like holidays or school breaks. Therefore, time-oriented data need to be 721 1-4244-1306-0/07/$25.00 ©2007 IEEE