EuroVA15
https://diglib.eg.org:443/handle/10.2312/12462
ISBN 978-3-905674-86-62024-03-29T14:26:00ZIntegrating Predictions in Time Series Model Selection
https://diglib.eg.org:443/handle/10.2312/eurova.20151107.073-077
Integrating Predictions in Time Series Model Selection
Bögl, Markus; Aigner, Wolfgang; Filzmoser, Peter; Gschwandtner, Theresia; Lammarsch, Tim; Miksch, Silvia; Rind, Alexander
E. Bertini and J. C. Roberts
Time series appear in many different domains. The main goal in time series analysis is to find a model for given time series. The selection of time series models is done iteratively based, usually, on information criteria and residual plots. These sources may show only small variations and, therefore, it is necessary to consider the prediction capabilities in the model selection process. When applying the model and including the prediction in an interactive visual interface it is still difficult to compare deviations from actual values or benchmark models. Judging which model fits the time series adequately is not well supported in current methods. We propose to combine visual and analytical methods to integrate the prediction capabilities in the model selection process and assist in the decision for an adequate and parsimonious model. In our approach a visual interactive interface is used to select and adjust time series models, utilize the prediction capabilities of models, and compare the prediction of multiple models in relation to the actual values.
2015-01-01T00:00:00ZExploration and Assessment of Event Data
https://diglib.eg.org:443/handle/10.2312/eurova.20151106.067-071
Exploration and Assessment of Event Data
Bodesinsky, Peter; Alsallakh, Bilal; Gschwandtner, Theresia; Miksch, Silvia
E. Bertini and J. C. Roberts
Event data is generated in many domains, like business process management, industry or healthcare. These datasets are often unstructured, exhibit variant behaviour, and may contain errors. Before applying automated analysis methods, such as process mining algorithms, the analyst needs to understand the dependency between events in order to decide which analysis method might fit the recorded events. We define a categorization scheme of event dependencies and describe a preliminary approach for exploring event data, combining visual exploration with pattern mining. Events of interest can be selected, grouped, and visually explored, using either a sequential or a temporal scale. We present two use cases with shopping event data and report expert feedback on our approach.
2015-01-01T00:00:00ZGnaeus: Utilizing Clinical Guidelines for Knowledge-assisted Visualisation of EHR Cohorts
https://diglib.eg.org:443/handle/10.2312/eurova.20151108.079-083
Gnaeus: Utilizing Clinical Guidelines for Knowledge-assisted Visualisation of EHR Cohorts
Federico, Paolo; Unger, Jürgen; Amor-Amorós, Albert; Sacchi, Lucia; Klimov, Denis; Miksch, Silvia
E. Bertini and J. C. Roberts
The advanced visualization of electronic health records (EHRs), supporting a scalable analysis from single patients to cohorts, intertwining patients' conditions with executed treatments, and handling the complexity of timeoriented data, is an open challenge of visual analytics for health care. We propose an approach that, according to the knowledge-assisted visualization paradigm, leverages the domain knowledge acquired by clinical experts and formalized into computer-interpretable guidelines (CIGs), in order to improve the automated analysis, the visualization, and the interactive exploration of EHRs of patient cohorts. In this way, the analyst can get insights about the clinical history of multiple patients and assess the effectiveness of their health care treatments.
2015-01-01T00:00:00ZVisual Analytics and Uncertainty: Its Not About the Data
https://diglib.eg.org:443/handle/10.2312/eurova.20151104.055-059
Visual Analytics and Uncertainty: Its Not About the Data
MacEachren, Alan M.
E. Bertini and J. C. Roberts
Uncertainty visualization research has a long history, with contributions from scientific, information, geo-graphic and other visualization perspectives as well as from cognitive and HCI perspectives. But we still do not have generally accepted strategies for leveraging visualization to cope with uncertainty. Here, I argue that taking a visual analytics rather than visualization perspective can overcome this inertia. While uncertainty visualization research has focused on visually signifying and interacting with data uncertainty, taking a visual analytics approach recognizes that the challenge is about much more than uncertain data. The larger challenge is to enable reasoning under uncertainty (in all its forms). In this short paper, I sketch elements of what we know and outline some key challenges for developing visual analytics methods and tools that enable users to cope with uncertainty throughout the processes of sensemaking, decision-making, and action-taking.
2015-01-01T00:00:00Z