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PlanetData: Consuming Structured
        Data at Web Scale

   Elena Simperl, Barry Norton, Karlsruhe Institute of Technology

1st International Symposium on Data-driven Process Discovery and Analysis

                 June 30, 2011, Campione d’Italia, Italy
PlanetData‘s Aim and Objectives

   Aim: establish an interdisciplinary,
    sustainable European community on
    large-scale data management
    ◦ Purposeful data exposure
                                                         Databases

    ◦ Novel and improved applications
                                                                Data and
                                                  Semantics       Web
                                                                 Mining




•   Objectives
    ◦   Addressing challenges through integrated research
    ◦   Data and technology provisioning through PlanetData Lab
    ◦   Impact through training, dissemination, standardization
        and networking
    ◦   Openness and flexibility through PlanetData Programs
Work Plan Highlights
 Methods and techniques to publish, access and manage stream-
  like data
 Quality assessment of interlinked data sets, including best
  practices for the representation and usage of spatio-temporal
  information
 Provenance and access control framework for Linked (Stream)
  Data

   Data sets and vocabularies, including best practices for
    publishing and managing self-descriptive data

   Linked Services and Processes as an instrument to develop
    applications

 Yearly summer school co-located with the Extended Semantic
  Web Conference
 Semantic Web video journal

   PlanetData Programs
The Rise of Linked Data




     8/10/2011       Slide 4 of x
Data.gov & public sector information
   Many data sets useful for business
    intelligence
BBC & Media
   Value of content increased by Linked Data
BestBuy & eCommerce
   Structured mark-up increases visibility
Linked Data Cloud
   Taken together Linked Data is said to form
    a ‘cloud’ of shared references and
    vocabularies




                              (growing on a weekly basis)
Linked Data Principles
    1.   Use URIs as names for things
    2.   Use HTTP URIs so that people can look up
         those names.
    3.   When someone looks up a URI, provide useful
         information, using the standards (RDF,
         SPARQL)
    4.   Include links to other URIs, so that they can
         discover more things.

   Bring together semantic technologies and the
    Web architecture
   Applied to other types of data as well: stream-
    like, multimedia…
Consuming Linked Data




     8/10/2011    Slide 10 of x
Services Over Linked Data
   A problem can be seen in the
    current Linked Data sphere
    when it comes to
    services/APIs/functionalities

   The standards are often not
    then used

   The results of service
    interaction do not
    contribute to the Linked
    Data cloud

   Developers have to work
    with heterogeneous
    representations                 RDF
RDF Services at the BBC
    This is not a problem of scale, efficiency
     or speed




                                               RDF-based
                                               communication
                                               efficiently
                                               realised using
                                               memcached

    04.08.201   Real-time updates to a large
        0
                (ferocious) audience
Linked Open Services
   Aim to promote services over Linked Data
    bringing together:

   RESTful services (respecting Web
    architecture)
    ◦ Resource-oriented
    ◦ Manipulated with HTTP verbs
      GET, PUT (, PATCH), POST, DELETE
    ◦ Negotiate representations
   Linked Data
    ◦ Uniform use of URIs
    ◦ Use of RDF and SPARQL
Linked Services: Principles
   Concretely, Linked Open Services come with a
    set of guiding principles:
    1. Describe services as LOD prosumers
     with input and output descriptions as SPARQL graph
     patterns
    2. Communicate RDF by RESTful content negotiation
    3. Communicate and describe the knowledge
     contribution resulting from service interaction,
     including implicit knowledge relating input, output and
     service provider
   Associated with the last principle is an optional
    fourth:
    4. When wrapping non-LOS services, extend the (lifted,
     if non-RDF) message to make explicit the implicit
     knowledge, and to use Linked Data vocabularies, using
     SPARQL CONSTRUCT queries
                http://www.linkedopenservices.org/blog/?page_id=2
LOS Weather Service




    Input: [a wgs84:Point; wgs84:lat ?lat; wgs84:long ?long]
    Output:[met:weatherObservation [
             weather:hasStationID ?icao
             geonames:inCountry ?country;
             ...
             weather:hasWindEvent
                [weather:windDirection ?windDirection],
                [weather:windSpeed ?windSpeed]
Linked Processes: Principles
   In order to compose Linked Services we are
    not specific about the style, except that RDF
    must be stored and forwarded

   Principles:
    ◦ Decide control flow conditions based on SPARQL
      ASK queries
    ◦ Base iteration on SPARQL SELECT queries
    ◦ Define dataflow/mediation based on SPARQL
      CONSTRUCT queries

   In this way compositions, ‘mash-up’s, etc.,
    also use the languages/technologies most
    familiar to the Linked Data community
LOP Media Monitoring Process
   A Social Media Manager is required to monitor
    (micro)blogging sites and respond to negative comments:




                             10.08.2011
Composition Service 1
   A service may monitor the ‘Twittersphere’ for tweets with a
    given tag

Harvest
Input: {?t a sioc_t:Tag; rdfs:label ?l}
Output: {?p a sioc_t:MicroblogPost;
            sioc:topic ?t;
            sioc:has_creator ?m;
            sioc:content ?c .
            OPTIONAL {?p sioc:addressed_to ?a}}




                               10.08.2011
Composition Service 2
   A sentiment analysis service may annotate (micro)blog posts
    according to, e.g., the Human Emotion Ontology

AnalyseSentiment
Input: {?p a sioc:Post; sioc:content ?c}
Output: {?e a heo:Emotion;
            heo:hasManifestationInMedia ?p;
            heo:hasCategory ?c}




                              10.08.2011
Composition Service 3
   A human service selects among possible combinations of
    these and optionally raises a response

ManageMicroblog
Input: {?p a sioc_t:MicroblogPost;
           sioc:has_creator ?m.
        ?e heo:hasManifestationInMedia ?p.
        {?e heo:hasCategory heo:anger UNION
         ?e heo:hasCategory heo:disgust}}
Output: {OPTIONAL {?r a sioc_t:MicroblogPost;
                   sioc:addressed_to ?m}}



                             10.08.2011
PlanetData Collaborations




       8/10/2011      Slide 22 of x
http://www.planet-data.eu
Join PlanetData
   Associate partners have
      Access to open training infrastructure
      Early access to ongoing PD results through
       participation in PlanetData meetings
      Opportunity to shape the results and topics of the
       PD Programs through contribution of
       requirements and use cases
   PlanetData Programs call in 2012

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  • 1. PlanetData: Consuming Structured Data at Web Scale Elena Simperl, Barry Norton, Karlsruhe Institute of Technology 1st International Symposium on Data-driven Process Discovery and Analysis June 30, 2011, Campione d’Italia, Italy
  • 2. PlanetData‘s Aim and Objectives  Aim: establish an interdisciplinary, sustainable European community on large-scale data management ◦ Purposeful data exposure Databases ◦ Novel and improved applications Data and Semantics Web Mining • Objectives ◦ Addressing challenges through integrated research ◦ Data and technology provisioning through PlanetData Lab ◦ Impact through training, dissemination, standardization and networking ◦ Openness and flexibility through PlanetData Programs
  • 3. Work Plan Highlights  Methods and techniques to publish, access and manage stream- like data  Quality assessment of interlinked data sets, including best practices for the representation and usage of spatio-temporal information  Provenance and access control framework for Linked (Stream) Data  Data sets and vocabularies, including best practices for publishing and managing self-descriptive data  Linked Services and Processes as an instrument to develop applications  Yearly summer school co-located with the Extended Semantic Web Conference  Semantic Web video journal  PlanetData Programs
  • 4. The Rise of Linked Data 8/10/2011 Slide 4 of x
  • 5. Data.gov & public sector information  Many data sets useful for business intelligence
  • 6. BBC & Media  Value of content increased by Linked Data
  • 7. BestBuy & eCommerce  Structured mark-up increases visibility
  • 8. Linked Data Cloud  Taken together Linked Data is said to form a ‘cloud’ of shared references and vocabularies (growing on a weekly basis)
  • 9. Linked Data Principles 1. Use URIs as names for things 2. Use HTTP URIs so that people can look up those names. 3. When someone looks up a URI, provide useful information, using the standards (RDF, SPARQL) 4. Include links to other URIs, so that they can discover more things.  Bring together semantic technologies and the Web architecture  Applied to other types of data as well: stream- like, multimedia…
  • 10. Consuming Linked Data 8/10/2011 Slide 10 of x
  • 11. Services Over Linked Data  A problem can be seen in the current Linked Data sphere when it comes to services/APIs/functionalities  The standards are often not then used  The results of service interaction do not contribute to the Linked Data cloud  Developers have to work with heterogeneous representations RDF
  • 12. RDF Services at the BBC  This is not a problem of scale, efficiency or speed RDF-based communication efficiently realised using memcached 04.08.201 Real-time updates to a large 0 (ferocious) audience
  • 13. Linked Open Services  Aim to promote services over Linked Data bringing together:  RESTful services (respecting Web architecture) ◦ Resource-oriented ◦ Manipulated with HTTP verbs  GET, PUT (, PATCH), POST, DELETE ◦ Negotiate representations  Linked Data ◦ Uniform use of URIs ◦ Use of RDF and SPARQL
  • 14. Linked Services: Principles  Concretely, Linked Open Services come with a set of guiding principles: 1. Describe services as LOD prosumers with input and output descriptions as SPARQL graph patterns 2. Communicate RDF by RESTful content negotiation 3. Communicate and describe the knowledge contribution resulting from service interaction, including implicit knowledge relating input, output and service provider  Associated with the last principle is an optional fourth: 4. When wrapping non-LOS services, extend the (lifted, if non-RDF) message to make explicit the implicit knowledge, and to use Linked Data vocabularies, using SPARQL CONSTRUCT queries http://www.linkedopenservices.org/blog/?page_id=2
  • 15. LOS Weather Service Input: [a wgs84:Point; wgs84:lat ?lat; wgs84:long ?long] Output:[met:weatherObservation [ weather:hasStationID ?icao geonames:inCountry ?country; ... weather:hasWindEvent [weather:windDirection ?windDirection], [weather:windSpeed ?windSpeed]
  • 16. Linked Processes: Principles  In order to compose Linked Services we are not specific about the style, except that RDF must be stored and forwarded  Principles: ◦ Decide control flow conditions based on SPARQL ASK queries ◦ Base iteration on SPARQL SELECT queries ◦ Define dataflow/mediation based on SPARQL CONSTRUCT queries  In this way compositions, ‘mash-up’s, etc., also use the languages/technologies most familiar to the Linked Data community
  • 17. LOP Media Monitoring Process  A Social Media Manager is required to monitor (micro)blogging sites and respond to negative comments: 10.08.2011
  • 18. Composition Service 1  A service may monitor the ‘Twittersphere’ for tweets with a given tag Harvest Input: {?t a sioc_t:Tag; rdfs:label ?l} Output: {?p a sioc_t:MicroblogPost; sioc:topic ?t; sioc:has_creator ?m; sioc:content ?c . OPTIONAL {?p sioc:addressed_to ?a}} 10.08.2011
  • 19. Composition Service 2  A sentiment analysis service may annotate (micro)blog posts according to, e.g., the Human Emotion Ontology AnalyseSentiment Input: {?p a sioc:Post; sioc:content ?c} Output: {?e a heo:Emotion; heo:hasManifestationInMedia ?p; heo:hasCategory ?c} 10.08.2011
  • 20. Composition Service 3  A human service selects among possible combinations of these and optionally raises a response ManageMicroblog Input: {?p a sioc_t:MicroblogPost; sioc:has_creator ?m. ?e heo:hasManifestationInMedia ?p. {?e heo:hasCategory heo:anger UNION ?e heo:hasCategory heo:disgust}} Output: {OPTIONAL {?r a sioc_t:MicroblogPost; sioc:addressed_to ?m}} 10.08.2011
  • 21. PlanetData Collaborations 8/10/2011 Slide 22 of x
  • 22. http://www.planet-data.eu Join PlanetData  Associate partners have  Access to open training infrastructure  Early access to ongoing PD results through participation in PlanetData meetings  Opportunity to shape the results and topics of the PD Programs through contribution of requirements and use cases  PlanetData Programs call in 2012