Analytics
4 statements.
| Author | Tags | Primary Session | Secondary Sessions | Position Statement |
|---|---|---|---|---|
| Dan Andreescu | Analytics, Big Data, Collaboration, Documentation, Open Source, Research, Third Parties | Research, Analytics, and Machine Learning | Embracing Open Source Software |
Our strategic goals include scaling our communities to a truly global level, and expanding our understanding of human knowledge. To do this, in my opinion, we need to have a much better understanding of our communities' actual work. We have tens of thousands of people doing millions of hours of work every month, and nobody knows exactly what is being done, what the definition of "done" is, and how fast or slow the progress is. We are the leaders of the free knowledge movement, and we are mostly blind except for some big picture notions like pageviews and edits. It is my opinion that we need to develop a good understanding of the work being done on the wikis. Very capable people have already spent lots of time trying to do this, but I believe we have largely failed because of technical limitations. This is a big data and big compute problem, and we have not yet approached it as such. A close collaboration between our communities, Analytics, Research, and Audiences teams is needed, as well as the power of the WMF Hadoop cluster. I have had sessions on this topic already, and am excited to finish planning and transition to actual work. There are some very valuable implications of taking on and finishing this work. Most importantly, we will all be able to more objectively talk about frustrations in the community over changes that cause "more work". For example, when we launched Visual Editor there was huge backlash about the amount of work this change implied for our community. But because this was largely based on subjective opinions, emotions got involved and it took years to calm the negative effect of those emotions. This effort would also give us, for the first time, a way to celebrate these millions of hours of work. People could see, share, and take pride in their part of building human knowledge (if they wanted to, privacy is of course one of our top priorities). I am also interested in expanding our Open Source efforts, and examining changes that we can make to spur more collaboration. My reading of the strategic goals for 2030 is that the WMF will not have enough resources to execute by itself. That's where collaboration will be crucial, and where problems like in-house developed libraries without true Open Source presence will slow us down. We let documentation and third-party user support lag behind because we're busy with other stuff, and that's arguably fine for our scale so far. But this approach will not allow us to grow the way our Strategy is defined. |
| Erik Bernhardson | Analytics, Collaboration, Machine Learning, Open Source, Privacy, Structured Data | Research, Analytics, and Machine Learning |
Title: Empowering Editors with Machine Learning Background: Advances in machine learning, powered by open source libraries, is becoming the foundational backbone of technology organizations the world over. Many tedious, time consuming, tasks that previously required 100% human involvement can now be augmented with human in the loop machine learning to empower editors to get more done with the limited time they have available to contribute to the sum of all human knowledge. Advice: 1) Invest directly in applying known quantity machine learning, such as pre-trained ImageNet classifiers, to add structured data to our multimedia repositories to increase their discoverability. Perhaps via tools that provide editors with lists of appropriate items that they can easily click to add if appropriate to the multimedia. 2) Engage academia to work with Wikimedia data sets and employ developers to move the most promising results from research into production. There is already a significant amount of work being done in academia to test and evaluate machine learning with our data sets, but little to none of that work ever makes it back into Wikimedia sites. With more focus on collaboration we can encourage research that is specifically applicable to deployment goals. 3)Wikimedia has the ability to collect significant amounts of implicit user data via browsing sessions, searches, watchlists, editing histories, etc. that can be used for machine learning purposes. We need to be continuously thoughtful of the privacy implications of how we use this data. | |
| Giuseppe Lavagetto | Analytics, Data Center, Infrastructure, Job Queue, Multi-Datacenter, Performance | Evolving the MediaWiki Architecture |
The future of the MediaWiki infrastructure at the wikimedia foundation. MediaWiki is at the core of the infrastructure that serves all of the Wikimedia projects, and the current setup of MediaWiki in production poses various challenges: from the future of our current runtime (HHVM), to the to ability to serve MediaWiki from multiple DataCenters, to long standing issues as resource usage efficiency and flexibility. Here are some of the things we have to tackle in the future. Transition off of HHVM: Since the HHVM team has made it clear they're parting ways with full PHP compatibility, and that maintaining support for both HHVM and PHP in MediaWiki would be arduous, we need to make plans to move off of HHVM, back to PHP 7.x. This transition, while technically necessary, should not come at a cost for our users: page load times should not degrade. We can proceed by marking responses coming from either engine, collecting metrics and analyzing data. In order to achieve this, we should run the two runtimes in parallel on the same servers (which have plenty of capacity, given no MediaWiki cluster has an utilization over 40%), and we will then be able to programmatically route individual users or a percentage of traffic, or even specific wikis, to one or the other. The deadline for this transition is set for the end of 2018 (EOL of the last compatible version of HHVM), and planning and resources should be allocated to this goal. Multi-Datacenter support: We currently are using our datacenters in a active/passive setting, as far as MediaWiki is concerned. While this is ok in line of principle, this is a huge waste of resources and means we both have 50% of our servers doing nothing at all, and also limits our ability to expand the number of core datacenters we can use. Diverting the read load to secondary datacenters could also allow us to use caching in a less aggressive way when not needed. There is already a program underway to add first-class multi-DC support to MediaWiki, so we can focus on what specifically needs to be done in order to achieve this longtime goal: our final goal should be to serve reader's traffic from all datacenters, and to be able to switch the "master" datacenter in matter of minutes. Elasticity, resource usage efficiency At the moment , our infrastructure is plainly inadequate to react to sudden spikes of non-wiki content production and to changes that generate a lot of asynchrounous jobs, as a change of a popular template. The issues with the current jobqueue are widely known and publicized, but even the current transition to a new model won't solve the starvation of resources that result in a degraded user experience. Moreover, a single editor uploading videos via video2commons can easily overflow our media processing capacity for weeks. This happens because we allocate our resource statically (we have 4 vidoesclaers per datcenter, for example), we have an inefficient resource consumption, and reallocating servers requires time and effort. Modern applications stacks are elastic, meaning the operation of scaling up or down the capacity of a single cluster or functionality can be handled programmatically and/or manually whenever the need occurs, allowing the infrastructure to react to such changes. For economic and privacy/security reasons, Wikimedia doesn't make use of external cloud services, so the only way to achieve such flexibility is to build a serviceable infrastructure that can serve MediaWiki and any other project Wikimedia will support: the effort to do that is underway with the rollout of our Kubernetes-based IaaS in production. I think we should work, sooner than later, at moving the MediaWiki application stack (and maybe its semi-ephemeral caching) to the kubernetes platform. While the advantages of such an approach seem clear, it won't come without costs: specifically habits around code deployment, testing and configuration changes will need to be completely revisited and superseded by new approaches. | |
| Thomas Pellissier Tanon | Analytics, API, Collaboration, JavaScript, Lua, Mobile, Multimedia, Structured Data, User Experience, Wikibase, Wikidata | Knowledge as a Service |
Title: Content structuration and metadata are keys to fulfil our strategy Content: The Wikimedia mouvement strategy is making a focus on serving more different kinds of knowledge and sharing them with allies and partners [1]. I believe that the most important ground work for reaching these goals is to focus on the outgoing project of moving MediaWiki from a "wikitext plus media file" collaboration system to a platform allowing people to be able to collaborate on many kind of contents and to organise them in a cohesive way. Two axes seem important to me to pursue this goal: 1. Support a broader set of different contents, not just wikitext, Wikibase items and Lua/JavaScript/CSS contents but also images, sounds, movies, books, etc., that should bee editable just like wiki text pages in order to allow people to improve them in a collaborative way. 2. Build platforms and tools allowing contributors to create and clean metadata about these contents in order to build together the broadest cohesive set of knowledge ever available and increase its reusability. Going in these directions would allow us to:
|
