NDLI: Multiplicative synopses for relative-error metrics

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Multiplicative synopses for relative-error metrics

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Multiplicative synopses for relative-error metrics

Content Provider	ACM Digital Library
Author	Karras, Panagiotis
Abstract	Existing hierarchical summarization techniques fail to provide synopses good in terms of relative-error metrics. This paper introduces multiplicative synopses: a summarization paradigm tailored for effective relative-error summarization. This paradigm is inspired from previous hierarchical index-based summarization schemes, but goes beyond them by altering their underlying data representation mechanism. Existing schemes have decomposed the summarized data based on sums and differences of values, resulting in what we call additive synopses. We argue that the incapacity of these models to handle relative-error metrics stems exactly from this additive nature of their representation mechanism. We substitute this additive nature by a multiplicative one. We argue that this is more appropriate for achieving low-relative-error data approximations. We develop an efficient linear-time dynamic programming scheme for one-dimensional multiplicative synopsis construction under general relative-error-based metrics, and a special scheme for the case of maximum relative error. We generalize our schemes to higher data dimensionality and we show a surprising additional benefit gained by our special scheme for maximum relative error in this case. In our experimental study, we verify the higher efficacy of our model on relative-error-oriented summarization problems.
Starting Page	756
Ending Page	767
Page Count	12
File Format	PDF
ISBN	9781605584225
DOI	10.1145/1516360.1516447
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-03-24
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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