_Airport Noise NIMBYism: An Empirical Investigation_

october 2016 by jm

'Generally, a very small number of people account for a disproportionately high share of the total number

of noise complaints. In 2015, for example, 6,852 of the 8,760 complaints submitted to Ronald Reagan

Washington National Airport originated from one residence in the affluent Foxhall neighborhood of

northwest Washington, DC. The residents of that particular house called Reagan National to express irritation about aircraft noise an average of almost 19 times per day during 2015.'

Somebody needs help.

airports
noise
nimby
nimbyism
complaints
dc
of noise complaints. In 2015, for example, 6,852 of the 8,760 complaints submitted to Ronald Reagan

Washington National Airport originated from one residence in the affluent Foxhall neighborhood of

northwest Washington, DC. The residents of that particular house called Reagan National to express irritation about aircraft noise an average of almost 19 times per day during 2015.'

Somebody needs help.

october 2016 by jm

_Dynamic Histograms: Capturing Evolving Data Sets_ [pdf]

(via d2fn)
via:d2fn
histograms
streaming
big-data
data
dvo
dc
sado
dado
dynamic-histograms
papers
toread

may 2013 by jm

Currently, histograms are static structures: they are created from scratch periodically and their creation is based on looking at the entire data distribution as it exists each time. This creates problems, however, as data stored in DBMSs usually varies with time. If new data arrives at a high rate and old data is likewise deleted, a histogram’s accuracy may deteriorate fast as the histogram becomes older, and the optimizer’s effectiveness may be lost. Hence, how often a histogram is reconstructed becomes very critical, but choosing the right period is a hard problem, as the following trade-off exists: If the period is too long, histograms may become outdated. If the period is too short, updates of the histogram may incur a high overhead.

In this paper, we propose what we believe is the most elegant solution to the problem, i.e., maintaining dynamic histograms within given limits of memory space. Dynamic histograms are continuously updateable, closely tracking changes to the actual data. We consider two of the best static histograms proposed in the literature [9], namely V-Optimal and Compressed, and modify them. The new histograms are naturally called Dynamic V-Optimal (DVO) and Dynamic Compressed (DC). In addition, we modified V-Optimal’s partition constraint to create the Static Average-Deviation Optimal (SADO) and Dynamic Average-Deviation Optimal (DADO) histograms.

(via d2fn)

may 2013 by jm

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