×
  • Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
 

Data Science Provenance: From Drug Discovery to Fake Fans

by CTO at Musicmetric on Nov 11, 2013

  • 776 views

Knowledge work adds value to raw data; how this activity is performed is critical for how reliably results can be reproduced and scrutinized. With a brief diversion into epistemology, the presentation ...

Knowledge work adds value to raw data; how this activity is performed is critical for how reliably results can be reproduced and scrutinized. With a brief diversion into epistemology, the presentation will outline the challenges for practitioners and consumers of Big Data analysis, and demonstrate how these were tackled at Inforsense (life sciences workflow analytics platform) and Musicmetric (social media analytics for music).

The talk covers the following issues with concrete examples:
- Representations of provenance
- Considerations to allow analysis computation to be recreated
- Reliable collection of noisy data from the internet
- Archiving of data and accommodating retrospective changes
- Using linked data to direct Big Data analytics

Statistics

Views

Total Views
776
Views on SlideShare
678
Embed Views
98

Actions

Likes
0
Downloads
0
Comments
0

5 Embeds 98

http://strataconf.com 38
https://twitter.com 35
http://eventifier.co 17
http://www.linkedin.com 5
http://eventifier.com 3

Accessibility

Categories

Upload Details

Uploaded via SlideShare as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
Post Comment
Edit your comment

Data Science Provenance: From Drug Discovery to Fake Fans Data Science Provenance: From Drug Discovery to Fake Fans Presentation Transcript