Aggregation   5605

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Logstash: Collect, Parse, Transform Logs | Elastic
Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously, transforms it, and then sends it to your favorite “stash.”
log  logging  search  aggregation 
21 hours ago by bezthomas
ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization
Peter Bailis strikes again.

'Time series visualization of streaming telemetry (i.e., charting of
key metrics such as server load over time) is increasingly prevalent
in recent application deployments. Existing systems simply plot the
raw data streams as they arrive, potentially obscuring large-scale
deviations due to local variance and noise. We propose an alternative:
to better prioritize attention in time series exploration and
monitoring visualizations, smooth the time series as much as possible
to remove noise while still retaining large-scale structure. We
develop a new technique for automatically smoothing streaming
time series that adaptively optimizes this trade-off between noise
reduction (i.e., variance) and outlier retention (i.e., kurtosis). We
introduce metrics to quantitatively assess the quality of the choice
of smoothing parameter and provide an efficient streaming analytics
operator, ASAP, that optimizes these metrics by combining techniques
from stream processing, user interface design, and signal
processing via a novel autocorrelation-based pruning strategy and
pixel-aware preaggregation. We demonstrate that ASAP is able to
improve users’ accuracy in identifying significant deviations in time
series by up to 38.4% while reducing response times by up to 44.3%.
Moreover, ASAP delivers these results several orders of magnitude
faster than alternative optimization strategies.'
dataviz  graphs  metrics  peter-bailis  asap  smoothing  aggregation  time-series  tsd 
13 days ago by jm
python - When to use association, aggregation, composition and inheritance? - Stack Overflow
class A(object):
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c

def addNums():
self.b + self.c

class B(object):
def __init__(self, d, e, A):
self.d = d
self.e = e
self.A = A

def addAllNums(self):
x = self.d + self.e + self.A.b + self.A.c
return x

ting = A("yo", 2, 6)
ling = B(5, 9, ting)

print ling.addAllNums()
python  classes  association  aggregation 
29 days ago by MrToms
Principles for effective risk data aggregation and risk reporting
This article is written by basel committee and referred by certified FRM (Financial Risk Manager) exam. It mentions the principles of risk data aggregation: accuracy and integrity, completeness, timeliness and adaptability.
basel  risk-reporting  risk  data  aggregation 
6 weeks ago by peng.zhang
[1604.04660] Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like
The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane's performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial *general* intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A *task theory* would enable addressing tasks at the *class* level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.
artificial-intelligence  philosophy-of-engineering  rather-interesting  representation  approximation  aggregation  nudge-targets  consider:looking-to-see  consider:performance-space-analysis 
6 weeks ago by Vaguery

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