Exploring Change

      Tobias Bleifuß1 , Leon Bornemann1 , Theodore Johnson2 , Dmitri V.
           Kalashnikov2 , Felix Naumann1 , and Divesh Srivastava2
            1
             Hasso Plattner Institute, University of Potsdam, Germany
           {tobias.bleifuss,leon.bornemann,felix.naumann}@hpi.de
                         2
                           AT&T Labs–Research, USA
                   {johnsont,dvk,divesh}@research.att.com



      Abstract. Data and schema in datasets experience many different kinds
      of change: values are inserted, deleted or updated; rows appear and dis-
      appear; 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 specific prop-
      erty or entity? How dirty is the data? The fact that data changed can
      hint at different 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) proto-
      type 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].




References
1. Bleifuß, T., Bornemann, L., Johnson, T., Kalashnikov, D.V., Naumann, F., Srivas-
   tava, D.: Exploring change - A new dimension of data analytics. PVLDB 12(2),
   85–98 (2018)
2. Bleifuß, T., Bornemann, L., Kalashnikov, D.V., Naumann, F., Srivastava, D.:
   DBChEx: Interactive exploration of data and schema change. In: Proceedings of
   the Conference on Innovative Data Systems Research (CIDR) (2019)




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