New PDF release: Advances in Intelligent Data Analysis IX: 9th International

By Paul R. Cohen, Niall M. Adams, Michael R. Berthold

ISBN-10: 3642130623

ISBN-13: 9783642130625

This e-book constitutes the refereed complaints of the ninth foreign convention on clever info research, IDA 2010, held in Tucson, AZ, united states in might 2010. The 21 revised papers offered including 2 invited papers have been conscientiously reviewed and chosen from greater than forty submissions. All present facets of clever facts research are addressed, really clever aid for modeling and reading advanced, dynamical structures. themes coated are end-to-end software program structures; modeling complicated platforms reminiscent of gene regulatory networks, financial platforms, ecological platforms, assets reminiscent of water, and dynamical social structures resembling on-line groups; and robustness, scaling houses and different usability concerns.

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Read or Download Advances in Intelligent Data Analysis IX: 9th International Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010. Proceedings PDF

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Extra info for Advances in Intelligent Data Analysis IX: 9th International Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010. Proceedings

Example text

Correlation dimension. The correlation dimension algorithm effectively measures the fraction of points in state space (R12 ) that are -close to each other as scales from zero. , fractal) set, this number should grow via a power law, with non-integer power. , the cache behavior of the same code on two different computers. To compute it, one plots the logarithm of the correlation sum—a quantity defined in detail in [2]—versus , as shown in Figure 1(c), and looks for horizontal regions in the curve. 013.

The correlation dimension algorithm effectively measures the fraction of points in state space (R12 ) that are -close to each other as scales from zero. , fractal) set, this number should grow via a power law, with non-integer power. , the cache behavior of the same code on two different computers. To compute it, one plots the logarithm of the correlation sum—a quantity defined in detail in [2]—versus , as shown in Figure 1(c), and looks for horizontal regions in the curve. 013. , that the memory use of this code varies from run to run in a manner that depends strongly on small perturbations.

1 Introduction Traditional performance analysis methods used by the computer systems community implicitly assume that the systems under study are linear and time invariant. Most also treat these systems as stochastic. It has recently been established, however, that computers are actually deterministic nonlinear dynamical systems[1,9]. This not only calls into question those prior analyses—whose underlying assumptions are not in accord with these conditions—but also suggests a new dynamics-based methodology for computer performance analysis.

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Advances in Intelligent Data Analysis IX: 9th International Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010. Proceedings by Paul R. Cohen, Niall M. Adams, Michael R. Berthold


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