16 resultados para Barrot, Odilon, 1791-1873.

em Boston University Digital Common


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Digitized from a letter in the Drew University Methodist Collection. 1 item (4 p.); 19.5 x 32 cm.

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Digitized from a letter in the Drew University Methodist Collection. 1 Item (4 p.); 20.5 x 33 cm.

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Digitized from a letter in the Drew University Methodist Collection. 1 Item (2 p.); 17 x 20 cm

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PDF file

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Memoriam.

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http://www.archive.org/details/metlakahtltruena00davirich

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http://www.archive.org/details/pioneeringincong00spririch

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http://www.archive.org/details/westernmissionsa00smetrich

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http://www.archive.org/details/boundbrookoramyr00greeiala

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http://www.archive.org/details/johnwesleytheman00pikeuoft

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http://digilib.bu.edu/archive/strangerthanfict00halcrich/strangerthanfict00halcrich.djvu

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Temporal locality of reference in Web request streams emerges from two distinct phenomena: the popularity of Web objects and the {\em temporal correlation} of requests. Capturing these two elements of temporal locality is important because it enables cache replacement policies to adjust how they capitalize on temporal locality based on the relative prevalence of these phenomena. In this paper, we show that temporal locality metrics proposed in the literature are unable to delineate between these two sources of temporal locality. In particular, we show that the commonly-used distribution of reference interarrival times is predominantly determined by the power law governing the popularity of documents in a request stream. To capture (and more importantly quantify) both sources of temporal locality in a request stream, we propose a new and robust metric that enables accurate delineation between locality due to popularity and that due to temporal correlation. Using this metric, we characterize the locality of reference in a number of representative proxy cache traces. Our findings show that there are measurable differences between the degrees (and sources) of temporal locality across these traces, and that these differences are effectively captured using our proposed metric. We illustrate the significance of our findings by summarizing the performance of a novel Web cache replacement policy---called GreedyDual*---which exploits both long-term popularity and short-term temporal correlation in an adaptive fashion. Our trace-driven simulation experiments (which are detailed in an accompanying Technical Report) show the superior performance of GreedyDual* when compared to other Web cache replacement policies.