Browsing by Author "Turkay, Cagatay"

Now showing 1 - 6 of 6
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    A Descriptive Framework for Stories of Algorithms
    (The Eurographics Association, 2019) Liem, Johannes; Henkin, Rafael; Wood, Jo; Turkay, Cagatay; Madeiras Pereira, João and Raidou, Renata Georgia
    Data-driven stories, widely used in journalism and scientific communication, match well with the recent focus on interpretable machine learning and AI explainability. Current technologies allow authors to break away from narratives that reflect traditional analytical workflows. To support designing such types of stories, we introduce a descriptive framework that helps identifying narrative patterns and other characteristics of algorithm-related stories. We describe the design space within the framework and demonstrate how to apply to an example of an algorithm-centered story, discussing potential future steps.
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    Dissecting Visual Analytics: Comparing Frameworks for Interpreting and Modelling Observed Visual Analytics Behavior
    (The Eurographics Association, 2020) Brown, Vanessa; Turkay, Cagatay; Jianu, Radu; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    This paper provides an empirical, comparative exploration of the role of analytic frameworks in interpreting and modelling visual analytics behavior through data gathered in observational studies. The crucial research on understanding the complex and multi-faceted interplay between visual analytics tools and their users is often done through controlled or naturalistic observations of analysts engaging in the visual analytic process, followed by the interpretation of the observation data. The researchers in Human Computer Interaction and Cognitive Sciences have long used structured analytic frameworks for such analyses, where a guiding set of principles and questions direct attention to relevant aspects of the studied behavior, eventually leading to more complete and consistent analyses. Such frameworks are rarely applied in the visualization domain however, and information about how to apply them and their benefits is scarce. With this paper, we contribute a comparative account, grounded in empirical data collected in a user study with 10 participants using Tableau to analyze domain-specific data, of the types of insights we can glean from interpreting observational data using three different frameworks: Joint Action Theory, Distributed Cognition, and Situated Cognition.
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    EuroVis 2022 CGF 41-3 STARs: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Bruckner, Stefan; Turkay, Cagatay; Vrotsou, Katerina; Bruckner, Stefan; Turkay, Cagatay; Vrotsou, Katerina
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    Towards a WHAT-WHY-HOW Taxonomy of Trajectories in Visualization Research
    (The Eurographics Association, 2019) Allain, Kevin; Turkay, Cagatay; dykes, jason; Madeiras Pereira, João and Raidou, Renata Georgia
    Effective analysis of movement often requires a comprehensive approach where computational and visual methods are combined to address a wide variety of tasks involving movers with diverse characteristics. In order to help the process of designing effective methods for a wide range of movement analysis cases, we develop a provisional taxonomy that links what Brehmer et al. [BM13] term statements of WHY-WHAT-HOW with tasks, types of movers, context and methods used to compute or visualize data. Within this document we present the origin of this taxonomy, the process we followed to populate it, discuss the novel categories within it, and finally use it to explore relationships between elements of trajectory analysis. Our main contribution is to provide a new means of connecting elements of WHY-WHAT-HOW when analysing trajectories.
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    Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Sondag, Max; Turkay, Cagatay; Xu, Kai; Matthews, Louise; Mohr, Sibylle; Archambault, Daniel; ; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    Epidemiologists use individual-based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex 'infection maps' of time-varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time-varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
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    Visual Analytics of Event Data using Multiple Mining Methods
    (The Eurographics Association, 2019) Adnan, Muhammad; Nguyen, Phong; Ruddle, Roy; Turkay, Cagatay; Landesberger, Tatiana von and Turkay, Cagatay
    Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very different domains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by combining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patterns of missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. However, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI steps allowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study's aim was to understand users' behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users. Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For the latter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detected subtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives, complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data.