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

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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  • 1. End-to-End Semantics Sensors Semitones Science Social Machines David De Roure
  • 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.
  • 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 andProcess measured into 100ml RB flask. flourinatedRecord 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. Content Navigation throughoutthe 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
  • 6. Some  Social  Machines  
  • 7. 1 Music Sensor NetworksScience Social2 3
  • 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. 1 Music Sensor NetworksScience Social2 3
  • 10.
  • 11. “Twelve months ago, there werethree of us in the new OlympicData Team: … Today, we are ateam of 20, we have built fiveapplications, provide 174endpoints, manage 50 messagequeues and support ten separateBBC Olympic products - from thesport website to the InteractiveVideo Player.”
  • 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. Segment  Ontology   class structureOntology 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. 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. story arcs andtimey-wimey stuff Mike Jewell Faith Lawrence
  • 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. 1 Music Sensor NetworksScience Social2 3
  • 19. ...the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites Source: CERN, CERN-EX-0712023,
  • 20. method     data  
  • 21. Co-­‐Evolu7on  of  Research  Objects   Packs   ORE   OAI   Workflows   Research  Objects   W3C  PROV   Computa7onal  Research  Objects  
  • 22. Notifications and automatic re-runs Autonomic Self-repair Curation New research?Machines are users too
  • 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. 1 Music Sensor NetworksScience Social2 3
  • 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. Building  a  Social  Machine  Virtual World(Network of social interactions) Dave  Robertson   Model of social interactionDesign and Participation andComposition Data supply Physical  World   (people  and  devices)  
  • 27.
  • 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. 1 Music Sensor NetworksScience Social2 3
  • 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. 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. The brain opera technology: New instrumentsand gestural sensors for musical interactionand performance. Journal of New MusicResearch, 28(2), 130–149.
  • 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.  @dder    Slide  credits:  ,  Jeremy  Frey,  Chris7ne  Borgman,  Faith  Lawrence  &  Mike  Jewell,  Ichiro  Fujinaga,  Stephen  Downie,  Kevin  Page,  Ben  Fields,  Carole  Goble,  Dave  Robertson      
  • 35. Links  •  Seman7c  Media   hWp://    •  Music  Informa7on  Retrieval  Evalua7on  eXchange  (MIREX)   hWp://­‐    •  Workflow  Forever  project  (Wf4Ever)   hWp://www.wf4ever-­‐    •  Future  of  Research  Communica7on  (FORCE11)   hWp://    •  Theory  and  Prac7ce  of  Social  Machines  (SOCIAM)   hWp://    •  W3C  SSN  Community  Group   hWp://­‐cg/    
  • 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  •  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