Abstract
Practical applications often need to rank multi-variate records by assigning various priorities to different attributes. Consider a relation that stores students’ grades on two courses: database and algorithm. Student performance is evaluated by an “overall score” calculated as w 1 · g db + w 2 · g alg , where w 1, w 2 are two input “weights”, and g db (g alg ) is the student grade on database (algorithm). A “top-k ranked query” retrieves the k students with the best scores according to specific w 1 and w 2.
We focus on top-k queries whose k is bounded by a constant c, and present solutions that guarantee low worst-case query cost by using provably the minimum space. The core of our methods is a novel concept, “minimum covering subset”, which contains only the necessary data for ensuring correct answers for all queries. Any 2D ranked search, for example, can be processed in O(log B (m/B) + c/B) I/Os using O(m/B) space, where m is the size of the minimum covering subset, and B the disk page capacity. Similar results are also derived for higher dimensionalities and approximate ranked retrieval.
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Tao, Y., Hadjieleftheriou, M. (2006). Processing Ranked Queries with the Minimum Space. In: Dix, J., Hegner, S.J. (eds) Foundations of Information and Knowledge Systems. FoIKS 2006. Lecture Notes in Computer Science, vol 3861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11663881_17
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DOI: https://doi.org/10.1007/11663881_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31782-1
Online ISBN: 978-3-540-31784-5
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