Theresia Gschwandtner

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Data quality is a vital topic for business analytics in order to gain accurate insight and make correct decisions in many data-intensive industries. Albeit systematic approaches to categorize, detect, and avoid data quality problems exist, the special characteristics of time-oriented data are hardly considered. However, time is an important data dimension(More)
Poor data quality leads to unreliable results of any kind of data processing and has profound economic impact. Although there are tools to help users with the task of data cleansing, support for dealing with the specifics of time-oriented data is rather poor. However, the time dimension has very specific characteristics which introduce quality problems,(More)
Mapping medical concepts from a terminology system to the concepts in the narrative text of a medical document is necessary to provide semantically accurate information for further processing steps. The MetaMap Transfer (MMTx) program is a semantic annotation system that generates a rough mapping of concepts from the Unified Medical Language System (UMLS)(More)
A number of studies have investigated different ways of visualizing uncertainty. However, in the temporal dimension, it is still an open question how to best represent uncertainty, since the special characteristics of time require special visual encodings and may provoke different interpretations. Thus, we have conducted a comprehensive study comparing(More)
Visual Analytics methods are used to guide domain experts in the task of model selection through an interactive visual exploration environment with short feedback cycles. Evaluation showed the benefits of this approach. However, experts also expressed the demand for prediction capabilities as being already important during the model selection process.(More)
The visual exploration and analysis of time-oriented data in healthcare are important yet challenging tasks. This position paper presents six challenges for Visual Analytics in healthcare: (1) scale and complexity of time-oriented data, (2) intertwining patient condition with treatment processes, (3) scalable analysis from single patients to cohorts, (4)(More)
Clinical practice guidelines are documents that include recommendations describing appropriate care for the management of patients with a specific clinical condition, such as diabetes or chronic heart failure. Several representation languages exist to model these documents in a computer-interpretable and -executable form with the intention of integrating(More)
Abstract Data Mining on time-oriented data has many real-world applications, like optimizing shift plans for shops or hospitals, or analyzing traffic or climate. As those data are often very large and multi-variate, several methods for symbolic representation of time-series have been proposed. Some of them are statistically robust, have a lower-bound(More)