International Federation for Information Processing
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 with distinct characteristics which affords special consideration in the context of dirty data. Building upon existing taxonomies of general data quality problems, the authors address 'Dirty' time-oriented data, i.e., time-oriented data with potential quality problems.