Data and schema in datasets experience many di erent kinds of change: values are inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, and so on. In such a dynamic situation, users might wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How volatile are the values of a speci c property or entity? How dirty is the data? The fact that data changed can hint at di erent hidden processes: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. To interactively explore such changes, we present our DBChEx (Database Change Explorer) prototype system [2]. Using two real-world datasets, IMDB and Wikipedia infoboxes, we illustrate how users can gain valuable insights into data generation processes and data or schema evolution over time by a mix of serendipity and guided investigation using DBChEx. Finally, we identify a range of technical challenges that need to be addressed to fully realize our vision of change exploration [1].
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