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Title: Categorical skylines for streaming data
Authors: Sarkas, N.
Koudas, N.
Das, G.
Tung, A.K.H. 
Keywords: Categorical
Data stream
Partial order
Issue Date: 2008
Citation: Sarkas, N.,Koudas, N.,Das, G.,Tung, A.K.H. (2008). Categorical skylines for streaming data. Proceedings of the ACM SIGMOD International Conference on Management of Data : 239-250. ScholarBank@NUS Repository.
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. Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376643
Appears in Collections:Staff Publications

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