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.
End-to-End Semantics: Sensors, Semitones and Social Machines
End-to-End Semantics Sensors Semitones Science Social Machines David De Roure
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?
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
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 semanticmedia.org.uk
“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.”http://www.bbc.co.uk/blogs/bbcinternet/
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
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
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
story arcs andtimey-wimey stuff Mike Jewell Faith Lawrence
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
Co-‐Evolu7on of Research Objects Packs ORE OAI Workﬂows Research Objects W3C PROV Computa7onal Research Objects
Notifications and automatic re-runs Autonomic Self-repair Curation New research?Machines are users too
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, …
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
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)
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
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
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!
The brain opera technology: New instrumentsand gestural sensors for musical interactionand performance. Journal of New MusicResearch, 28(2), 130–149.
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)
firstname.lastname@example.org 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
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/
• 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