Abstract
Preference queries have been largely studied for relational systems but few proposals exist for stream data systems. Most of the existing proposals concern the skyline, top-k or top-k dominating queries, coupled with the sliding-window operator. However, user preferences queries on data streams may be more sophisticated than simple skyline or top-k and may involve more expressive operations on streams. This paper improves the existing work on data stream query-answering personalization by proposing a solution to express and handle contextual preferences together with a large variety of queries including one-shot and continuous queries. It adopts a more expressive preference model supporting context-based preferences, allowing to capture a wide range of situations. We propose algorithms to implement the new preference operators on stream data and validate their performance on a real-world dataset of stock market streams.
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Petit, L., de Amo, S., Roncancio, C., Labbé, C. (2012). Top-k Context-Aware Queries on Streams. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32600-4_29
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DOI: https://doi.org/10.1007/978-3-642-32600-4_29
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