Finding Your Way in Chronoland: Visual Metaphors for Orientation of Temporal Data Explorers

Finding Your Way in Chronoland: Visual Metaphors for Orientation of Temporal Data Explorers Michael Smuc (michael.smuc@donau-uni.ac.at) Eva Mayr (eva.mayr@donau-uni.ac.at) Hanna Risku (hanna.risku@donau-uni.ac.at) Florian Windhager (florian.windhager@donau-uni.ac.at) Research Center KnowComm, Danube University Krems, Dr.-Karl-Dorrek-Str. 30 3500 Krems, Austria Abstract In the following, we will use situated, embodied cognition as the theoretical basis, explaining why visual metaphors are central to human thinking and, thus, also potentially appropriate as a structuring principle for the presentation of complex heterogeneous data. We suggest using visual metaphors for structuring and presenting the underlying structures of complex information for communication purposes, using the “Chronoland” 1 -landscape metaphor in our example. With their deeper procedural knowledge, experts are superior to beginners in solving ill-defined problems. To support beginners, we suggest using a method for presenting complex, heterogeneous data that can serve as a good basis for visualizing its inherent information. To demonstrate this approach, we conducted focus group interviews with temporal data analysts to identify their problem solving strategies. To make the results usable as a scaffold for beginners in the domain, we designed a visualization based on a metaphor of the flow of time and water – Chronoland. By providing a landscape-like decision support system, experts and beginners alike are assisted on their path towards a problem’s solution. Situated, Embodied Cognition Traditional cognitive scientific theories aim at simulating human problem solving through computation. They are based on a propositional theory of mind: Cognition is seen as a linguistic activity in which symbols are manipulated using fixed rules (computational-representational understanding of mind – CRUM, Thagard, 1996, pp.10ff). In order to design systems suitable for such information processing, presentation of data in a symbolic and rule- based manner seems appropriate. However, human cognition and interaction have proved to be quite different to the abstract code switching operations seen in computerized information processing. It is being argued that human understanding is not based on a structural, syntactic analysis of linguistically transparent material. This is why logically structured corpi, semantic networks and other well-ordered, well-defined systems quickly reach their limits as mirrors of the mind when faced with “real-life” situations and ill-defined problems, primarily as a result of their ignorance of the actual textual and situative context. Indeed, human cognition and interaction prove to be far more flexible and constructive than had been assumed by the grammatically and semantically “correct” programs prior to the birth of various approaches of situated, embodied cognition in the 1980s. From these perspectives, the brain is no longer seen as a database, but as a dynamic, holistic network able to create patterns of activation – our memories are rich with all manners of different scenes, tones, smells, tastes, motions and emotions. As a result, CRUM and its symbolic models were substituted for example by experience-based, non-linguistic and emotionally loaded metaphors (Lakoff, 1987), action- Keywords: visual metaphors, situated cognition, human- computer interaction, knowledge management, information design, temporal data analysis Introduction Complex, ill-defined problems are a major challenge for experts in different domains. For example, personnel planning analysts and consultants analyze time-oriented personnel data and have to identify factors influencing personnel requirements (Smuc et al., 2008). For such a task, there is no single correct solution and no single best way to reach a solution. Instead experts have to engage in multiple rounds of problem solving, are often confronted with dead ends, and have to start from the beginning once more. These task and process characteristics resemble those of ill-defined problems (cp. Schraw, Dunkle, & Bendixen, 1995). An important factor in solving ill-defined problems is domain expertise: Experts have a better idea of how and when to apply different strategies (Schunn, McGregor, & Saner, 2005). Therefore, an important question which is addressed in this paper is how novice users can be supported in acquiring and using this knowledge of how and when to use which problem solving strategy. To address this question, we review different methods for generating visualization taxonomies and ontologies. Applying a bottom-up strategy, we discuss problem-solving strategies with experts in focus groups and generate information maps from their results. These maps serve as visual metaphors and, thereby, provide beginners with an overview of expert problem solving strategies and help them navigate the complex problem solving space of temporal data exploration. From the Greek Χρόνος (Chronos) meaning time.