Linked Data as a Service

3,136 views

Published on

Published in: Technology
0 Comments
4 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
3,136
On SlideShare
0
From Embeds
0
Number of Embeds
15
Actions
Shares
0
Downloads
134
Comments
0
Likes
4
Embeds 0
No embeds

No notes for slide

Linked Data as a Service

  1. 1. LINKED DATA AS A SERVICESEMTECHBIZ Berlin 2012Peter Haase, Michael Schmidtfluid Operations AG
  2. 2. fluid Operations (fluidOps)Linked Data & Semantic Technologies Enterprise Cloud ComputingSoftware company founded Q1/2008 by team of serial entrepreneurs, privatelyheld, VC fundedHeadquarters in Walldorf / Germany, SAP Partner PortCurrently 40 employeesNamed “Cool Vendor for SAP 2010” byGartner Mar 2010Global reseller agreement with EMC focus largeenterprise customers Apr 2010NetApp Advantage Alliance Partner Oct 2010
  3. 3. The Potential of Linked DataLinked Data• Set of standards, principles for publishing, sharing and interrelating structured knowledge• From data silos to a Web of Data• RDF as data model, SPARQL for querying• Ontologies to describe the semanticsBenefits of Linked Data in the Enterprise• Enterprise Data Integration: Semantically integrate and interlink data scattered among different information systems• Simplified publishing and sharing of data: Increase openness and accessibility of Enterprise Data• Enrichment and contextualization through interlinking: Value add by linking to Linked Open Data
  4. 4. Everything as a Service• Abstract from physical implementation details and location of resources• Regardless of geographic or organizational separation of provider and consumer• “In the cloud” Data as a Service• Web based• Virtualized Software as a Service• On-demand• Self-service Platform as a Service• Scalable• Pay as you go Infrastructure as a ServiceNext generation of XaaS is centered around the power of data.
  5. 5. Data-as-a-Service “Like all members of the "as a Service” family, DaaS is based on the concept that the product, data in this case, can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer.” Source: Wikipedia• Abstraction layer for data access abstract the applications from the specific setup of the data management service (such as local vs. remote, federation, and distribution)• Enabling automation of discovery, composition, and use of datasetsNext generation of XaaS is centered around the power of data. 5
  6. 6. Data-as-a-Service – Beyond Data Access• Data Markets: make it easy to find data from secondary data sources, consume or acquire the data in a usable – and often unified – format• Online Visualization Services: allow users to upload data, make charts and visualizations and publish these to an online audience• Data Publishing Solutions: allow data owners to publish their data collections and make them available to an online audience• Data Aggregators: integrate, cleanse data from different sources to provide the aggregated data as a value added service• BI / Analytics as a Service: provide higher level analytics functionality (statistical analysis), reporting, predictive analytics See also: http://blog.datamarket.com/2010/10/24/data-as-a-service-market-definitions/
  7. 7. Information Workbench - Linked Data Platform Information Workbench:  Semantics- & Linked Data-based integration of private and public data sources  Intelligent Data Access and Analytics  Visual Exploration  Semantic Search  Dashboarding and Reporting  Collaboration and knowledge management platform  Wiki-based curation & authoring of data Semantic Web Data  Collaborative workflows 7
  8. 8. Enabling Data Access:Virtualization of Data Sources• Linked Data as abstraction layer for virtualized data access across data spaces• 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.• Enables data portability across current data silos• Platform independent data access 8
  9. 9. Enabling Data Discovery:Metadata about Data Sets• Metadata about data sources essential for dynamic discovery• Access to data registered at global registries, e.g. ckan.org, data.gov, …• Based on metadata vocabularies (voID, DCAT)• Sort/filter data sets by topic, license, size and many more facets to identify relevant data• Visually explore data sets
  10. 10. Enabling Data Composition:Federation of Virtualized Data Sources Application LayerVirtualization Layer Data Layer SPARQL SPARQL SPARQL SPARQL Endpoint Endpoint Endpoint Endpoint Metadata Registry Data Source Data Source Data Source Data SourceSee also: FedX: Optimization Techniques for Federated Query Processing on Linked Data (ISWC2011)
  11. 11. Semantic Wiki + Widgets asSelf-service Linked Data Frontend• Semantic Wiki for linking of unstructured and structured data• Declarative specification of the UI based on available pool of widgets and declarative wiki-based syntax• Widgets have direct access to the DB• Type-based template mechanism Wiki Page in Edit Mode … … and Displayed Result Page
  12. 12. Information Workbench:Data as a Service in a Cloud Platform Architecture Application Layer (SaaS) Provisioning, Monitoring and Management Virtualization Layer Infrastructure Layer (IaaS) Data Layer (DaaS) Netw.-Att. Storage Network Computing Resources Enterprise Data Sources Open Data Sources
  13. 13. Provisioning, Monitoring and Management Application Layer (SaaS) Virtualization Layer Infrastructure Layer (IaaS) Data Layer (DaaS) Netw.-Att. Storage Network Computing Resources Enterprise Data Sources Open Data Sources Self-service Data Integration Self-service UI Data Discovery Deployment & Federation & Analytics• Self-service deployment • On demand access to • Virtualized data • Living UI, composed of the Information private and public access from semantics-aware Workbench in the cloud data sources • Dynamic integration & widgets• Pay-per-use • Dynamic Discovery federation of data • Ad hoc data• Scalability on demand sources exploration, visualization, analytics
  14. 14. Information Workbench – Linked Data as a ServiceApplication AreasKnowledge Management in theLife SciencesDigital Libraries, Media andContent ManagementIntelligent Data CenterManagement
  15. 15. Example: Conference Explorer• „Linked-Data-a-Thon“: build an application that makes use of conference metadata and contextualizes data with external data sources in two weeks• Realized with the Information Workbench http://semtech2012.fluidops.net/ Data Sources Features • Conference Metadata (Linked Data) • Conference • Public bibliographic meta data schedule, timelines, hot topics • Social Networks: • Statistics and reports • Twitter • Background information about • Facebook authors and publications • LinkedIn • Link to social network profiles and • LinkedGeoData statistics 15
  16. 16. Example: A Cloud Portal for Access to Open Datawith the Information WorkbenchGoal ... using the• Collect meta data from global data markets (LOD Cloud, WorldBank, CKAN, …) fluid Operations• Allow integrated search and ad hoc integration of data Technology Stack sources from different repositories• Link data with private/internal data sources, if desired• Support semi-automated linking between data sets• Provide visualization, exploration, and analytics functionality on top of integrated data sourcesRealization• Currently running project with the Hasso Plattner Institute (Potsdam, Germany)• Create local repository containing data market metadata• Use self-service technology to make services publicly available + Information Workbench for analytics
  17. 17. Example: Linked Data in Pharma Main Use Cases • Integrate data from company-internalSearch, Interrogate and Visualize, Analyze and Capture and Augment Reason Explore Knowledge data silos • Augment company- Integrated data graph over all data sources internal data with Integ Linked Open Data • Collaborative knowledge management • Support of internal processes (drug development)Private Data Sources Public Data Sources
  18. 18. Example: Dynamic Semantic PublishingOlympics 2012 requirements• A lot of output... Page per Athlete [10,000+], Page per country [200+], Page per Discipline [400-500], Time coded, metadata annotated, on demand video, 58,000 hours of content• Almost real time statistics and live event pages with too many web pages for too few journalistsDynamic Semantic Publishing (DSP) architecture to automatecontent aggregation Information Workbench for DSP • Collaborative authoring and linking of unstructured and structured semantic data • Ontology and instance data management • DSP editorial workflows • Automation of content creation and enrichment
  19. 19. Visit us at our booth!CONTACT:fluid OperationsAltrottstr. 31Walldorf, GermanyEmail: peter.haase@fluidops.comwebsite: www.fluidops.com http://semtech2012.fluidops.net/Tel.: +49 6227 3846-527

×