Categorical skylines for streaming data

  • Authors:

  • Nikos Sarkas;Gautam Das;Nick Koudas;Anthony K. H. Tung

  • Affiliations:

  • University of Toronto, Toronto, ON, Canada;University of Texas at Arlington, Arlington, TX, USA;University of Toronto, Toronto, ON, Canada;National University of Singapore, Singapore, Singapore

  • Venue:
  • Proceedings of the 2008 ACM SIGMOD international conference on Management of data
  • Year:
  • 2008

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Abstract

The problem of skyline computation has attracted considerable research attention. In the categorical domain the problem becomes more complicated, primarily due to the partially-ordered nature of the attributes of tuples. In this paper, we initiate a study of streaming categorical skylines. We identify the limitations of existing work for offline categorical skyline computation and realize novel techniques for the problem of maintaining the skyline of categorical data in a streaming environment. In particular, we develop a lightweight data structure for indexing the tuples in the streaming buffer, that can gracefully adapt to tuples with many attributes and partially ordered domains of any size and complexity. Additionally, our study of the dominance relation in the dual space allows us to utilize geometric arrangements in order to index the categorical skyline and efficiently evaluate dominance queries. Lastly, a thorough experimental study evaluates the efficiency of the proposed techniques.