@misc{628,
  keywords = {data cleansing, Time-Oriented Data, automated methods},
  author = {Thomas Braunsberger},
  title = {Automatic Cleansing Operations of Time-Oriented Data},
  abstract = {<p>Time-oriented data are of great importance as they are found in almost any database. May it be in terms of a record of working hours or a detailed list of sales statistics in an online shop. However, as it is the case with any other data these records tend to contain errors and correcting them manually would require a lot of effort and time, and thus, high costs. Some estimations go so far as to say that up to 40% of data contains errors. There are many methods and tools that focus on cleansing 'dirty' data, however, they rarely focus on time-oriented data. Some tools may help with a few time-oriented data problems, but time is hardly considered to be the main target. Those, who set a goal to deal with 'dirty' time-oriented data are mostly focused on a visual representation to make the task of error detection easier for the user. This led us to implement a research prototype that provides (semi-)automatic operations in order to take care of many possible time-oriented quality problems. Most of them do not require any further knowledge of the methods applied and hence, are ready to use by a large audience. We have evaluated the prototype in a usability study and derived suggestions for possible improvement.</p>},
  year = {2016},
  journal = {Institute of Visual Computing and Human-Centered Technology},
  pages = {111},
  publisher = {TU Wien},
  address = {Vienna},
  doi = {10.34726/hss.2016.32205},
}