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An	
  Integrated	
  Socio-­‐Technical	
  
Crowdsourcing	
  Pla8orm	
  for	
  
Accelera;ng	
  Returns	
  in	
  eScience	
  
Karl	
  Aberer,	
  Alexey	
  Boyarsky,	
  
Philippe	
  Cudré-­‐Maurox,	
  Gianluca	
  
Demar-ni,	
  and	
  Oleg	
  Ruchayskiy	
  
Science	
  
Yesterday	
   Today	
  
GiIed	
  Individuals	
   Collabora;ve	
  Effort	
  
OPERA	
  Collabora;on	
  
Scien;st-­‐Computer	
  Symbiosis	
  
•  A	
  single	
  scien;st	
  has	
  no	
  more	
  the	
  capacity	
  to	
  
process	
  all	
  the	
  informa;on	
  
– High	
  complexity	
  of	
  systems	
  and	
  workflows	
  
– Various	
  fields	
  of	
  exper;se	
  involved	
  
•  New	
  discoveries	
  will	
  emerge	
  from	
  
community-­‐based	
  socio-­‐technical	
  systems	
  
Community-­‐based	
  Socio-­‐technical	
  Systems	
  
•  Such	
  pla8orms	
  will	
  be	
  useful	
  
– Locally	
  to	
  the	
  scien;st 	
  	
  
– By	
  extrac;ng	
  knowledge	
  used	
  globally	
  
•  They	
  will	
  enable	
  cross-­‐pollina;on	
  
– All	
  ar;facts	
  need	
  to	
  be	
  interoperable	
  
– Higher	
  order	
  logic	
  to	
  combine	
  them	
  
Science	
  
Tomorrow	
  
Collec;ve	
  Intelligence	
  
What	
  do	
  we	
  need?	
  
•  Highly-­‐expressive	
  machine-­‐readable	
  formats	
  
– Ontologies	
  of	
  unprecedented	
  quality	
  
– Implicit	
  knowledge	
  available	
  in	
  the	
  head	
  of	
  the	
  
experts	
  
•  Understanding	
  concepts,	
  assump;ons,	
  
phenomena,	
  abstrac;ons	
  
•  Create	
  a	
  mental	
  map	
  of	
  a	
  research	
  field	
  
•  Understand	
  analysis	
  methods	
  
A	
  Giant	
  Crowdsourcing	
  
Conceptualiza;on	
  Machine	
  
Towards	
  Self-­‐Awareness	
  
•  A	
  Scien;fic	
  infrastructure	
  
– Complex	
  ontological	
  networks	
  
– Capture	
  the	
  scien;fic	
  process	
  
– Automate	
  rou;ne	
  opera;ons	
  
– Share	
  scien;fic	
  ar;facts	
  
•  Experts	
  will	
  train	
  the	
  system	
  with	
  their	
  daily	
  
ac;vi;es	
  
An	
  “entropy-­‐reduc;on”	
  machine	
  
•  Relate	
  en;;es	
  
•  Provide	
  lineage	
  informa;on	
  
•  Discriminate	
  conflic;ng	
  informa;on	
  
•  Reason	
  and	
  infer	
  new	
  data	
  
The	
  Web:	
  a	
  Collec;ve	
  Intelligence	
  engine	
  
	
  
•  Informa;on	
  systems	
  are	
  not	
  instruments	
  
•  A	
  catalyst	
  for	
  the	
  scien;fic	
  progress	
  
•  Reason	
  and	
  combine	
  scien;fic	
  ar;facts	
  at	
  very	
  
large	
  scale	
  
•  Individual	
  scien;st	
  will	
  not	
  be	
  able	
  to	
  fully	
  
appreciate	
  models	
  and	
  methods	
  
Scien;fic	
  progress	
  
Time	
  
Now	
  

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An Integrated Socio/Technical Crowdsourcing Platform for Accelerating Returns in eScience

  • 1. An  Integrated  Socio-­‐Technical   Crowdsourcing  Pla8orm  for   Accelera;ng  Returns  in  eScience   Karl  Aberer,  Alexey  Boyarsky,   Philippe  Cudré-­‐Maurox,  Gianluca   Demar-ni,  and  Oleg  Ruchayskiy  
  • 2. Science   Yesterday   Today   GiIed  Individuals   Collabora;ve  Effort  
  • 4. Scien;st-­‐Computer  Symbiosis   •  A  single  scien;st  has  no  more  the  capacity  to   process  all  the  informa;on   – High  complexity  of  systems  and  workflows   – Various  fields  of  exper;se  involved   •  New  discoveries  will  emerge  from   community-­‐based  socio-­‐technical  systems  
  • 5. Community-­‐based  Socio-­‐technical  Systems   •  Such  pla8orms  will  be  useful   – Locally  to  the  scien;st     – By  extrac;ng  knowledge  used  globally   •  They  will  enable  cross-­‐pollina;on   – All  ar;facts  need  to  be  interoperable   – Higher  order  logic  to  combine  them  
  • 7. What  do  we  need?   •  Highly-­‐expressive  machine-­‐readable  formats   – Ontologies  of  unprecedented  quality   – Implicit  knowledge  available  in  the  head  of  the   experts   •  Understanding  concepts,  assump;ons,   phenomena,  abstrac;ons   •  Create  a  mental  map  of  a  research  field   •  Understand  analysis  methods  
  • 8. A  Giant  Crowdsourcing   Conceptualiza;on  Machine  
  • 9. Towards  Self-­‐Awareness   •  A  Scien;fic  infrastructure   – Complex  ontological  networks   – Capture  the  scien;fic  process   – Automate  rou;ne  opera;ons   – Share  scien;fic  ar;facts   •  Experts  will  train  the  system  with  their  daily   ac;vi;es  
  • 10. An  “entropy-­‐reduc;on”  machine   •  Relate  en;;es   •  Provide  lineage  informa;on   •  Discriminate  conflic;ng  informa;on   •  Reason  and  infer  new  data  
  • 11. The  Web:  a  Collec;ve  Intelligence  engine     •  Informa;on  systems  are  not  instruments   •  A  catalyst  for  the  scien;fic  progress   •  Reason  and  combine  scien;fic  ar;facts  at  very   large  scale   •  Individual  scien;st  will  not  be  able  to  fully   appreciate  models  and  methods