Many real-world datasets are incomplete due to factors such as data collection failures or
misalignments between fused datasets. Visualizations of incomplete datasets should allow …

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis.
Pre‐processing pipelines often require multiple routines to address data quality challenges …

Data quality management and assessment play a vital role for ensuring the trust in the data
and its fitness-of-use for subsequent analysis. The transformation history of a data wrangling …

Computational models, such as simulations, are central to a wide range of fields in science
and industry. Those models take input parameters and produce some output. To fully exploit …

The derivation of information from monitoring drinking water quality at high spatiotemporal
resolution as it passes through complex, ageing distribution systems is limited by the …

Data imputation is an essential pre-processing task for data governance, aimed at filling in
incomplete data. However, conventional data imputation methods can only partly alleviate …

When performing data analysis, people often confront data sets containing missing values.
We conducted an empirical study to understand the effect of visualizing those missing …

Documentation of data is critical for understanding the semantics of data, understanding
how data was created, and for raising aware-ness of data quality problem, errors, and …

Time series data are essential in many fields, like economics, natural sciences, and
medicine, to name a few. Measuring and recording these data allow us to document …

In multivariate time series analysis, pre-processing is integral for enabling analysis, but
inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty …