Howdy,
Happy to report that production[1] and development[2] sets of Discovery
Dashboards are up and running again, this time managed by Puppet. (There
was a bug with web proxies and DNS settings that delayed this
announcement.) Theoretically they should be snappier to use now because
there is no longer an extra virtualization (Vagrant) layer and they are
running directly on Labs instances.
R is a software and programming language mainly used for statistical
inference, machine learning, and data wrangling & visualization. RStudio's
Shiny[3] is a framework for developing web applications in R, and it's what
Discovery's dashboards are written in.
The Reading::Discovery::Analysis team (with guidance and help from
Guillaume Lederrey) is proud to announce a new module available in Ops'
Puppet repo: shiny_server[4], which installs & configures RStudio's Shiny
Server[5] for serving R/Shiny applications. The module also provides
resources for installing R packages from CRAN, GitHub, and other remote git
repositories like Gerrit. For a practical example, refer to Discovery
Dashboards base[6] and production[7] profiles.
Cheers,
Mikhail on behalf of Discovery Analysts
[1] https://discovery.wmflabs.org
[2] https://discovery-beta.wmflabs.org/
[3] https://shiny.rstudio.com/
[4] https://github.com/wikimedia/puppet/tree/production/modules/shiny_server
[5] https://www.rstudio.com/products/shiny/shiny-server/
[6]
https://github.com/wikimedia/puppet/blob/production/modules/profile/manifes…
[7]
https://github.com/wikimedia/puppet/blob/production/modules/profile/manifes…
Thanks to Erica Litrenta for sharing this with me. I thought I'd share if
forward.
"It was because of the letter K that I found my younger sister, but for 14
years, it was also the letter K that kept us apart."
https://www.wired.com/story/search-algorithms-kept-me-from-my-sister-for-14…
Yours,
Chris Koerner
Community Liaison
Wikimedia Foundation
Hello, This is the Discovery update for last week. Apologies for the
delay in getting it out.
== Discussions ==
=== Search ===
* Created a method for the Kafka consumer to take 'learn to rank'
queries from a queue and run them against ElasticSearch to generate
relevance labels [0]
* Added in the ability to use kafka in our LTRank feature generation
queries and pushing them into ElasticSearch for analysis [1]
* Added ability to extract TF and IDF based features in the
ElasticSearch 'learning to rank' plugin [2]
* A/B test still in progress 'explore similar' links, but we're
running into a few bugs that will be sorted out next week [3]
* Fixed a bug where searching for phrase queries did not highlight
page content [4]
=== Analysis ===
* Fixed a bug with the sister project snippets and eventlogging [5]
* Finished up analysis for determining what is a reasonable per-IP
ratelimit for maps [6]
* Fixed a minor dashboard bug (splines) [7]
[0] https://phabricator.wikimedia.org/T162059
[1] https://phabricator.wikimedia.org/T162072
[2] https://phabricator.wikimedia.org/T167437
[3] https://phabricator.wikimedia.org/T164856
[4] https://phabricator.wikimedia.org/T167798
[5] https://phabricator.wikimedia.org/T168916
[6] https://phabricator.wikimedia.org/T169175
[7] https://phabricator.wikimedia.org/T169125
Yours,
Chris Koerner
Community Liaison
Wikimedia Foundation
Very interesting!
10. jul. 2017 16:34 skrev "Chris Koerner" <ckoerner(a)wikimedia.org>:
Thanks to Erica Litrenta for sharing this with me. I thought I'd share if
forward.
"It was because of the letter K that I found my younger sister, but for 14
years, it was also the letter K that kept us apart."
https://www.wired.com/story/search-algorithms-kept-me-
from-my-sister-for-14-years
Yours,
Chris Koerner
Community Liaison
Wikimedia Foundation
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