Aggregation   5612

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To Cite or to Steal? When a Scholarly Project Turns Up in a Gallery
What I was seeing was an announcement for a show by Jason Shulman at Cob Gallery called Photographs of Films. The press and interviews collected on the gallery’s website lauded a conceptual beauty and rigor in his work, but the only thing I could see was a rip-off. “Email for price list.” These images were unmistakably similar to the distinctive work I had been producing for years, and it was not long before friends started writing to let me know.
copyright  art  aggregation  averaging  images  movies  rip-offs  jason-shulman  jason-salavon  kevin-l-ferguson 
yesterday by jm
News: Trinity Mirror launches InYourArea: InPublishing
For the first time local news, traffic & travel, weather, things to do, property prices, planning applications, public notices, crime statistics, police updates, retail news, food hygiene ratings and more will be accessible in one place. combines and aggregates this data in real time down to postcode level and the platform offers consistent, rich, real time information for each postcode without the dependency on needing a highly active community of contributors.

Darren Sher, Head of Emerging Products at Trinity Mirror, said: "There is a wealth of hyperlocal news, data and information in many different places, but it's fragmented and difficult to access in a user friendly way. InYourArea will change that. It has the potential to be for local news and information what search engines were for the internet: a gateway to vast amounts of data and information that is currently hard to access but highly relevant to people's lives.
Hyperlocal  people  tm  aggregation  personalisation  email  native  algorithms 
14 days ago by paulbradshaw
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 
8 weeks 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 
9 weeks ago by jm

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