Abstract / topic summary
Let's simply admit it:
**We all have big-data by now**
What if "messy data is okay and fine and can get better over time"?
I mean for any amount & size of arbitrary digital data?
Almost "on its own".
So, in big-data there is no messy data:
Maybe lack of patterns - and tools to look at them properly.
Big-data setups do this all the time.
It scales in hardware. Not workforce.
**Let's put the fun back into data again!**
Pour your datas into #DataGardens and watch your files and projects
evolve and grow into graphs of related annotated objects of all kinds.
Auto-grouping by a "magic syntax" in their IDs:
`❤️$TIMESTAMP*$CONTEXT*$RANDOM⭐️`
The tools needed exist, are #FOSS-licensed and enterprise-ready:
Just need to be orchestrated.
You'll LOVE THIS science-faction reality presentation!
Speaker summary
Peter Bubestinger-Steindl
Peter studied Media Computer Science at the Technical University in Vienna. He has worked as a project leader and developer in the field of digital archives since 2002, set up and developed solutions for national heritage-, television- and radio-archives as well as service-providers. From 2010 to 2015, he managed the video digitization development at the Österreichische Mediathek (Austria's national A/V archive) and was instrumental in discovering and improving the lossless video codec FFV1 for long-term preservation. Since 2014, he has specialized in coordinating and deploying paid Open Source solutions for high-quality, demanding use cases and has been invited as a consultant, trainer, speaker and lecturer by memory institutions, universities and companies around the globe.
