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|Title||Study of Factors Affecting the Problem and Task Characterization for Time-Stamped Event Sequences|
|Institution||University of Zurich|
|Faculty||Faculty of Business, Economics and Informatics|
|Abstract Text||Time-stamped event sequences (TSES) are event sequences without values. Analysts are mainly interested in the temporal signatures of phenomena. It is a hardly investigated data type with growing interest since it is observed across a wide range of domains. There are two main problems in TSES that hamper the design of visual-interactive solutions. First, lack of awareness of affecting factors for problem characterization for TSES and second, lack of specific task characterization for TSES. Consequently, designers have a hard time making correct design decisions when building data analysis solutions for TSES, which ultimately influence the effectiveness of the tool to be built. We conducted two types of studies to address these problems. To address the lack of awareness of affecting factors, we did a systematic characterization of TSES-oriented real-world problems structured by four main aspects: (1) domain context & users, (2) data characteristics, (3) tasks, (4) metrics. In our study approach, we systematically identified a diverse set of factors associated with these above-mentioned main four aspects initially. Then, we collected 65 TSES-oriented real-world problems spanning a wide range of domains, focusing on identified factors using a user-based survey study. Lastly, we systematically analyzed the discovered factors and then related them to identify the relationships between factors. To address the second problem of lack of specific task characterization, we presented a generalized problem characterization for TSES. In our study approach, we used two complementary survey sources: a User-based survey study and a Survey of design studies. Initially, we built a generalization of tasks that are currently supported in vis tools related to TSES based on 16 design studies for TSES and related, which resulted in 26 tasks. Then we built a generalization of user tasks based on 65 survey responses, which resulted in 25 tasks. For the generalization, we used coding and affinity diagramming, well-known techniques of qualitative research. Finally, we unified these two sources and proposed a task characterization consisting of 28 tasks for TSES. We found that questionnaire answers on TSES are extremely heterogeneous, and no two answers are similar or even equal with some degree of abstraction. Results of the second part of the study show that 80% of the user tasks are similar to tasks extracted from design studies focused on TSES or related data types.|