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End-to-End Semantics
       Sensors
      Semitones
        Science
    Social Machines

   David De Roure
Mo7va7on	
  

•  This	
  workshop	
  series	
  has	
  successfully	
  
   demonstrated	
  that	
  Seman7c	
  Web	
  
   te...
http://musicnet.mspace.fm/blog/music-linked-data-workshop/
To Do




                      Ingredient List                                 Dissolve 4-      Add K2CO3                ...
Content Navigation throughout
the Content Life-Cycle
•  Annota7on	
  should	
  occur	
  within	
  
   the	
  produc7on	
  ...
Some	
  Social	
  Machines	
  
1

           Music



           Sensor
          Networks

Science              Social
2                         3
A	
  Big	
  Picture	
  
                e-infrastructure


                Big Data                  The	
  Fourth	
  
   ...
1

           Music



           Sensor
          Networks

Science              Social
2                         3
http://www.slideshare.net/moustaki/linked-data-on-the-bbc-2638734
“Twelve months ago, there were
three of us in the new Olympic
Data Team: … Today, we are a
team of 20, we have built five
...
ê
INT.   VERSE   VERSE   BRIDGE VERSE   BRIDGE VERSE      OUT.




                                               Ichiro	...
Structural Analysis of Large Amounts of Music Information

    23,000 hours of   Digital	
  Music	
  
    recorded music
 ...
Segment	
  Ontology	
  

                                                              class structure




Ontology models...
Digital	
  Music	
  
                     Digital	
  Music	
  
                      Digital	
  Music	
  
                ...
story arcs and
timey-wimey stuff




                    Mike Jewell
                    Faith Lawrence
Music	
  

•  Industry	
  adop7on	
  (fits	
  with	
  prac7ce	
  and	
  solves	
  
   a	
  problem?)	
  
•  Significant	
  b...
1

           Music



           Sensor
          Networks

Science              Social
2                         3
...the imminent flood of
 scientific data expected
 from the next generation of
 experiments, simulations,
 sensors and sa...
method	
  
  	
  


 data	
  
Co-­‐Evolu7on	
  of	
  Research	
  Objects	
  
                           Packs	
  




                                  ...
Notifications and automatic re-runs
          Autonomic        Self-repair
           Curation
                      New r...
Science	
  
•  Systema7c	
  processing	
  of	
  (big)	
  data	
  
•  Par7cular	
  aWen7on	
  to	
  trust,	
  provenance,	
...
1

           Music



           Sensor
          Networks

Science              Social
2                         3
The	
  Order	
  of	
  Social	
  Machines	
  

  Real life is and must be full of all kinds of
  social constraint – the ve...
Building	
  a	
  Social	
  Machine	
  
Virtual World
(Network of
 social interactions)                                    ...
http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score/research-context
Social	
  

•  Sociotechnical	
  systems	
  perspec7ve	
  
   –  Fundamental	
  to	
  sensor	
  networks?	
  
   –  Closin...
1

           Music



           Sensor
          Networks

Science              Social
2                         3
Case	
  Study	
  1	
  
•  ICT-­‐enabled	
  manufacturing	
  (e.g.	
  a	
  phone,	
  car	
  parts)	
  
•  Sensors	
  throug...
Case	
  Study	
  2	
  	
  
•  Studio	
  as	
  sensor	
  network	
  
•  End	
  to	
  end	
  seman7cs	
  from	
  instrument	...
The brain opera technology: New instruments
and gestural sensors for musical interaction
and performance. Journal of New M...
Provoca7ons	
  
1.  Do	
  we	
  have	
  examples	
  of	
  end-­‐to-­‐end	
  seman7cs	
  in	
  SSN?	
  
2.  How	
  do	
  we...
david.deroure@oerc.ox.ac.uk	
  
www.oerc.ox.ac.uk/people/dder	
  
www.scilogs.com/eresearch	
  
@dder	
  
	
  
Slide	
  cr...
Links	
  
•  Seman7c	
  Media	
  
   hWp://seman7cmedia.org.uk/	
  	
  
•  Music	
  Informa7on	
  Retrieval	
  Evalua7on	
...
•    D.	
  De	
  Roure,	
  C.	
  Goble	
  and	
  R.	
  Stevens.	
  The	
  Design	
  and	
  Realisa7on	
  of	
  the	
  myEx...
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics: Sensors, Semitones and Social Machines
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Keynote talk by David De Roure at SSN workshop at ISWC 2012, Boston, 12 November 2012

In many respects the music industry has gone digital "end-to-end", with success stories in Semantic Web adoption. Science too is dealing with a "digital turn" and the R&D community is active with Semantic Web. Meanwhile the Semantic Sensor Network workshop series has demonstrated the applicability of Semantic Web approaches in the sensor network domain. Looking to the future, what can we learn from music and science – and what can they learn from us? In this talk I will draw examples from music and science and introduce a discussion on future work in Semantic Sensor Networks.

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End-to-End Semantics: Sensors, Semitones and Social Machines

  1. 1. End-to-End Semantics Sensors Semitones Science Social Machines David De Roure
  2. 2. Mo7va7on   •  This  workshop  series  has  successfully   demonstrated  that  Seman7c  Web   technologies  can  be  used  with  sensor   networks   •  It  hasn’t  necessarily  demonstrated  that   they  are  the  technology  of  choice   •  What  can  we  learn  from  the  applica7on  of   Seman7c  Web  elsewhere?  
  3. 3. http://musicnet.mspace.fm/blog/music-linked-data-workshop/
  4. 4. To Do Ingredient List Dissolve 4- Add K2CO3 Heat at reflux Cool and add Heat at Cool and add Extract with Combine organics, Remove Fuse compound to silica & List flourinated powder for 1.5 hours Br11OCB reflux until water (30ml) DCM dry over MgSO4 & solvent in column in ether/petrol Fluorinated biphenyl 0.9 g Br11OCB 1.59 g biphenyl in completion (3x40ml) filter vacuo A digital lab book Potassium Carbonate 2.07 g butanone Butanone 40 ml Plan replacement that Add Cool Add Reflux Liquid- Remove Column Add Reflux Cool Add Dry Filter Fuse liquid Solvent Chromatography extraction by Rotary Evaporation 0.9031 grammes Weigh Inorganics dissolve 2 layers. Added brine ~20ml. text image 3 of 40 Measure ml excess Measure g Silica Ether/ Petrol Ratio chemists were able to use, and liked. Sample of 4- Butanone dried via silica column and Process measured into 100ml RB flask. flourinated Record Used 1ml extra solvent to wash out biphenyl Annotate container. DCM MgSO4 Annotate 1 1 2 2 1 3 1 4 3 5 2 6 2 7 4 8 9 10 11 12 13 14 Add Cool Add Reflux Add Remove Column Add Reflux Cool Liquid- Dry Filter Fuse liquid (Buchner) Solvent Chromatography text Sample of Butanone Annotate extraction by Rotary Sample of Br11OCB Water Annotate Annotate Evaporation K2CO3 Measure Powder Weigh Weigh Measure text Started reflux at 13.30. (Had to change heater stirrer) Only reflux 40 text Washed MgSO4 with text ml for 45min, next step 14:15. Organics are yellow solution DCM ~ 50ml 2.0719 g g 30 ml 1.5918 Key Observation Types Future Questions Process weight - grammes Whether to have many subclasses of processes or fewer with annotations measure - ml, drops Combechem Input How to depict destructive processes Jeremy  Frey   annotate - text Literal 30 January 2004 How to depict taking lots of samples temperature - K, C ° gvh, hrm, gms Observation What is the observation/process boundary? e.g. MRI scan
  5. 5. Content Navigation throughout the Content Life-Cycle •  Annota7on  should  occur  within   the  produc7on  process   •  Integra7ng  knowledge  of  the   produc7on  workflow   •  Managing  and  exposing  this   metadata  using  modern  seman7c   web  and  linked  data  technology   •  Empowering  human  producers   and  consumers   semanticmedia.org.uk
  6. 6. Some  Social  Machines  
  7. 7. 1 Music Sensor Networks Science Social 2 3
  8. 8. A  Big  Picture   e-infrastructure Big Data The  Fourth   The Future! More machines Big Compute Quadrant     Conventional Social online Computation Networking R&D More people
  9. 9. 1 Music Sensor Networks Science Social 2 3
  10. 10. http://www.slideshare.net/moustaki/linked-data-on-the-bbc-2638734
  11. 11. “Twelve months ago, there were three of us in the new Olympic Data Team: … Today, we are a team of 20, we have built five applications, provide 174 endpoints, manage 50 message queues and support ten separate BBC Olympic products - from the sport website to the Interactive Video Player.” http://www.bbc.co.uk/blogs/bbcinternet/
  12. 12. ê INT. VERSE VERSE BRIDGE VERSE BRIDGE VERSE OUT. Ichiro  Fujinaga  
  13. 13. Structural Analysis of Large Amounts of Music Information 23,000 hours of Digital  Music   recorded music Collec7ons   Music Information Retrieval Community Student-­‐sourced   Community   ground  truth   SoLware   Supercomputer   Linked  Data   Repositories  
  14. 14. Segment  Ontology   class structure Ontology models properties from musicological domain •  Independent of Music Information Retrieval research and signal processing foundations •  Maintains an accurate and complete description of relationships that link them Kevin  Page  and  Ben  Fields  
  15. 15. Digital  Music   Digital  Music   Digital  Music   Digital  Music   Collec7ons   Collec7ons   Collec7ons   Collec7ons   ground  truth   ground  truth   ground  truth   Community   Community   Community   SoLware   SoLware   Exper7se   Exper7se   SoLware   Exper7se   Exper7se   Results   Results   papers   Results   Results   papers   papers   Evalua7on   Evalua7on   Papers   Infrastructure   Infrastructure   (sociotechnical)   Evalua7ons   Evalua7ons   (sociotechnical)   Evalua7ons  
  16. 16. story arcs and timey-wimey stuff Mike Jewell Faith Lawrence
  17. 17. Music   •  Industry  adop7on  (fits  with  prac7ce  and  solves   a  problem?)   •  Significant  benefit  of  automa7on  within  the   enterprise  and  for  public  use   •  R&D  community  has  created  socio-­‐technical   infrastructure   •  Pushing  towards  capture  at  source  
  18. 18. 1 Music Sensor Networks Science Social 2 3
  19. 19. ...the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites Source: CERN, CERN-EX-0712023, http://cdsweb.cern.ch/record/1203203
  20. 20. method     data  
  21. 21. Co-­‐Evolu7on  of  Research  Objects   Packs   ORE   OAI   Workflows   Research  Objects   W3C  PROV   Computa7onal   Research  Objects  
  22. 22. Notifications and automatic re-runs Autonomic Self-repair Curation New research? Machines are users too
  23. 23. Science   •  Systema7c  processing  of  (big)  data   •  Par7cular  aWen7on  to  trust,  provenance,   reproducibility   •  Assistance  versus  automa7on   –  Taylorisa7on,  provisional  outcomes   –  Trust  through  authority  or  the  crowd?   •  Scholarly  communica7on  supported  by   Seman7c  Web  “social  objects”   –  e.g.  Models,  mashups,  narra7ves,  …  
  24. 24. 1 Music Sensor Networks Science Social 2 3
  25. 25. The  Order  of  Social  Machines   Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration… The stage is set for an evolutionary growth of new social engines. Berners-Lee, Weaving the Web, 1999
  26. 26. Building  a  Social  Machine   Virtual World (Network of social interactions) Dave  Robertson   Model of social interaction Design and Participation and Composition Data supply Physical  World   (people  and  devices)  
  27. 27. http://www.bodleian.ox.ac.uk/bodley/library/special/projects/whats-the-score/research-context
  28. 28. Social   •  Sociotechnical  systems  perspec7ve   –  Fundamental  to  sensor  networks?   –  Closing  the  loop  needs  applica7ons  and  users   •  Ci7zen  sensing  as  social  machine   •  Developing  theory  and  prac7ce   –  Design  and  construc7on  of  social  machines  
  29. 29. 1 Music Sensor Networks Science Social 2 3
  30. 30. Case  Study  1   •  ICT-­‐enabled  manufacturing  (e.g.  a  phone,  car  parts)   •  Sensors  throughout  produc7on  process   •  Monitoring  en7re  product  lifecycle  –  life  of  objects   (internet  of  things)   •  Collec7ng  data  from  discourse  in  collabora7ve   engineering  process   •  Informa7on  privacy   •  Pursuing  “Crowd  and  cloud”  philosophy   •  Big  Data:  seek  to  detect  “signatures”  in  the  data  in   order  to  op7mise  the  manufacturing  process  
  31. 31. Case  Study  2     •  Studio  as  sensor  network   •  End  to  end  seman7cs  from  instrument  (sensor)  to   consumer  (almost  to  the  brain…)   •  Reproducible,  repurposeable,  reuseable,  remixable   music   •  Business  models  and  DRM   •  Social  Objects  =  Score,  MP3  file   Research  Object  =  mix   •  Interoperability  standards  (analogue  good  too!)   •  Dimension  of  performance,  crea7vity   •  Already  using  Seman7c  Web!  
  32. 32. The brain opera technology: New instruments and gestural sensors for musical interaction and performance. Journal of New Music Research, 28(2), 130–149.
  33. 33. Provoca7ons   1.  Do  we  have  examples  of  end-­‐to-­‐end  seman7cs  in  SSN?   2.  How  do  we  share  the  methods  for  processing  sensor   data?  (soLware,  workflows,  automa7on,  …)   –  Provenance,  trust,  reproducibility?  (Linked  Science,  Provenance)   3.  Are  we  considering  SSN  as  sociotechnical  systems?   –  What  are  the  social  objects  in  SSN?  (data?)   –  Social  life  of  objects  (bikes)   –  Sensors  as  Ci7zens!  S3N  =  Seman7c  Social  Sensor  Nets?  (Ruben)   –  What  are  the  social  machines  in  SSN?   4.  What  can  we  do  in  concert  with  science  and  music?   –  The  studio  as  sensor  network?  Crea7vity?  (ISWC2013  jammers)   5.  Can  our  community  build  a  sociotechnical  infrastructure   to  advance  the  work?   –  More  than  (one)  ontology  as  social  object?  (SSN  ontology)  
  34. 34. david.deroure@oerc.ox.ac.uk   www.oerc.ox.ac.uk/people/dder   www.scilogs.com/eresearch   @dder     Slide  credits:  ,  Jeremy  Frey,  Chris7ne  Borgman,  Faith  Lawrence  &  Mike  Jewell,   Ichiro  Fujinaga,  Stephen  Downie,  Kevin  Page,  Ben  Fields,  Carole  Goble,  Dave   Robertson       www.myexperiment.org/packs/347      
  35. 35. Links   •  Seman7c  Media   hWp://seman7cmedia.org.uk/     •  Music  Informa7on  Retrieval  Evalua7on  eXchange  (MIREX)   hWp://www.music-­‐ir.org/mirex/     •  Workflow  Forever  project  (Wf4Ever)   hWp://www.wf4ever-­‐project.org/     •  Future  of  Research  Communica7on  (FORCE11)   hWp://force11.org/     •  Theory  and  Prac7ce  of  Social  Machines  (SOCIAM)   hWp://sociam.org/     •  W3C  SSN  Community  Group   hWp://www.w3.org/community/ssn-­‐cg/    
  36. 36. •  D.  De  Roure,  C.  Goble  and  R.  Stevens.  The  Design  and  Realisa7on  of  the  myExperiment   Virtual  Research  Environment  for  Social  Sharing  of  Workflows  Future  Genera/on   Computer  Systems  25,  pp.  561-­‐567.     •  S.  Bechhofer,  I.  Buchan,  D  De  Roure  et  al.  Why  linked  data  is  not  enough  for  scien7sts,   Future  Genera/on  Computer  Systems   •  D.  De  Roure,  David  and  C.  Goble,  Anchors  in  ShiHing  Sand:  the  Primacy  of  Method  in   the  Web  of  Data.  WebSci10,  April  26-­‐27th,  2010,  Raleigh,  NC,  US.   •  D.  De  Roure,  S.  Bechhofer,  C.  Goble  and  D.  Newman,  Scien7fic  Social  Objects,  1st   Interna/onal  Workshop  on  Social  Object  Networks  (SocialObjects  2011).   •  D.  De  Roure,  K.  Belhajjame,  P.  Missier,  P.  et  al  Towards  the  preserva7on  of  scien7fic   workflows.  8th  Interna/onal  Conference  on  Preserva/on  of  Digital  Objects  (iPRES  2011).   •  Carole  A.  Goble,  David  De  Roure  and  Sean  Bechhofer  Accelera7ng  scien7sts’  knowledge   turns.  Will  be  available  at  www.springerlink.com   •  Khalid  Belhajjame,  Oscar  Corcho,  Daniel  Garijo  et  al  Workflow-­‐Centric  Research   Objects:  First  Class  Ci7zens  in  Scholarly  Discourse,  SePublica2012  at  ESWC2012,   Greece,    May  2012   •  Kevin  R.  Page,  Ben  Fields,  David  De  Roure  et  al  Reuse,  Remix,  Repeat:  The  Workflows  of   MIR,  13th  Interna7onal  Society  for  Music  Informa7on  Retrieval  Conference  (ISMIR   2012)  Porto,  Portugal,  October  8th-­‐12th,  2012   •  Jun  Zhao,  Jose  Manuel  Gomez-­‐Perezy,  Khalid  Belhajjame  et  al,  Why  Workflows  Break  -­‐   Understanding  and  Comba7ng  Decay  in  Taverna  Workflows,  eScience  2012,  Chicago,   October  2012  

Keynote talk by David De Roure at SSN workshop at ISWC 2012, Boston, 12 November 2012 In many respects the music industry has gone digital "end-to-end", with success stories in Semantic Web adoption. Science too is dealing with a "digital turn" and the R&D community is active with Semantic Web. Meanwhile the Semantic Sensor Network workshop series has demonstrated the applicability of Semantic Web approaches in the sensor network domain. Looking to the future, what can we learn from music and science – and what can they learn from us? In this talk I will draw examples from music and science and introduce a discussion on future work in Semantic Sensor Networks.

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