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Enterprise linked data clouds

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Slides by Dr. Giovanni Tummarello, CEO and Founder of SindiceTech at #Cloudbusting2012 14/09/12 on GMIT campus

Slides by Dr. Giovanni Tummarello, CEO and Founder of SindiceTech at #Cloudbusting2012 14/09/12 on GMIT campus

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Enterprise linked data clouds Enterprise linked data clouds Presentation Transcript

  • Enterprise Linked Data CloudsDr. Giovanni TummarelloDERI InstituteCEO SindiceTech
  • An “Intense” definition Enterprise – Linked Data – Clouds• Enterprise not all of them• Linked Data is not exactly what you get when you google up• Cloud has a double meaning
  • Knowledge Intensive Enterprises• Those that will live and dies by their ability to incorporate new diversely structured knowledge in their processes and products – Examples: • Health Care Life Science • Scientific and Technical Publishing • Defense, Intelligence • …
  • Example story (Pharmaceutical company0To stay competitive, Pharmaceutical companies need to leverage all the dataavailable from inside sources as well as from the increasingly many publicHCLS data sources available. Due to the diversity of this data with respect tonature, formats, quality, there are complex integration issues . Goals:• The ability to speed up “In silico” scientific workflows• The ability to create large scale “data maps” or “aggregated views”• The ability to receive recommendations and suggestions for new data connections• Provide their R&D departments with superior tools for investigating their internal knowledge; search engines and data browsing tools• The ability to leverage the ever increasing body of public, crowd curated open data4 of 16
  • A very simple HCLS data schema
  • Linked Data• We here refer to the basic tools of the “Semantic Web” – RDF – SPARQL – Little more 
  • Tim Berners LeeWWWSemantic Web
  • Tim Berners-Lee, CERN March 1989 Information Management: A Proposal
  • Data+Metadata, together.Metadata + Data  RDF Stream 
  • And this data..• IS BIG• Can be Fast• IS Extremely Variable• Gartner’s 3v: Volume Velocity Variability
  • Scale is only 1 dimensionMultiple dimensions of WeD data integration• RDF tool stack  flexibility• Cluster scalable processing  scalability• “Cloud” Pipelines  dynamicity
  • How we started : a search engine for the web of data (Sindice.com)Web of data 650,000,000 Knowledge Graphs  5 TB + of “Big Knowledge Data”data.
  • SindiceTech• Incorporating requirements from enterprises – Scientific and Technical content companies – Defense – Pharma and Biotech• Inheriting 5 years of IP with R&D on: – Semantic Technologies  RDF and a pragmatic stack around it – Handle very large amount of Knowledge Data • Hadoop/NOSQL • Semantic Information Retrieval
  • Source BI / DSSSystemsRDBMS Pivot Pipeline Composer UI Browser S3 Semantic IR (SIRen) SparQLed Loaders / Outbox Adaptors / Inbox Integration Transformati Solr HDFS on & Analytics No SQL FTP Pipeline RDBMS Semantic Layer (RDF) Event Logging (Splunk / Logstack) 3rd Party Big Data Layer (Hadoop, Hive, Pig) / Cloudera BI / DSS e.g. SASOther Cloud Layer (e.g. Amazon, Openstack) HPA Middleware for Big Knowledge Processing
  • Cloud SpaceSemantic Sandboxes16 of 16
  • Full Json Like Search. On Solr.All operators supported.
  • SIREn: Semantic IR Engine• Extension to Enterprise Search Engine Solr• Semantic, full-text, incremental updates, distributed search Semantic SIREn Databases Constant time
  • Relational Faceted Browsing. At speed of light Patent Pending
  • Initial customers
  • Thank youWith the contribution of