Not exact matches
The reason that today's
big data sets pose problems for existing memory management techniques, explains Saman Amarasinghe, a professor of electrical engineering and computer science, is not so
much that they are large as that they are what computer scientists call «sparse.»
«This is new, as previous studies had generally found the dates of origination to be older and not clustered in time — the current study uses a
much bigger genetic
data set than any of the earlier ones.»
I probably need the Complete Idiot's Guide, but what I get out of this is, using the mean of the whole
data set (if it does have an actual hocky stick shape) as zero creates a higher horizontal line from which all the
data vary in various amounts & it tends to «pull up» the negative differences & makes the positive differences look not so
big (or it makes all the
data look on average equally large in distance from the mean, both in pos & neg directions), making the whole thing look like nothing
much is happening, aside from cyclical changes.
So the RSS «pause» of not quite 19 years is 1) unique to that
data set; 2) not that
much longer than the Santer «minimum» span; and 3) defined by beginning with the largest El Nino in the modern record (unless and until the present one turns out
bigger).
The scenario encapsulates so
much BS from assumptions, ignorance of observational trends, rational action on
big and apparent dangers, and then there is the
data sets, the models, the potential for bias, did I mention the assumptions.
This thought - provoking statistic
set the stage for a
much - anticipated panel discussion at RISMedia's 2017 Real Estate CEO Exchange in New York on how brokers are leveraging the application of predictive analytics and
big data in their businesses to better serve consumers» real estate needs.