1 / 30

How to survive the document & data tsunami?

How to survive the document & data tsunami?. Lambda Verdonckt Business Analyst TenForce. 1. We know how to handle large data , regardless of the technology used. Semantic Technology. 2. The only purpose-built technology, to survive a tsunami of doc and data. Semantic Technology. 3.

libby
Télécharger la présentation

How to survive the document & data tsunami?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. How to survive the document & data tsunami? Lambda Verdonckt Business Analyst TenForce

  2. 1 We know how to handle large data, regardless of the technology used.

  3. Semantic Technology 2 The only purpose-built technology, to survive a tsunami of doc and data.

  4. Semantic Technology 3 Leveraging information in old systems, no need to change current way of working.

  5. How did we end up here in the first place?

  6. Semantic Technology Turns the web of documents into a web of data. Turns the web as a virtual library into a virtual database. TenForce applies these technologies in corporate environments.

  7. How to survive the document & data tsunami? Semantic Technology • State-of-the-art • Examples • Future

  8. Semantic Technology The meaning of the data is encoded separately The only purpose-built technology for handling a tsunami of data, in a flexible way. data model (JohnDoe, type, Customer) (JohnDoe, owns, Account123) (Account123, type, BankingAccount) Account owns Customer type Person => ontology, thesaurus, taxonomy etc. Software understands the data and can reason about it

  9. Semantic Technology Standards A set of standards & tools to work with large data sets

  10. Semantic Technology Architectures

  11. TenForce Semantic Offering Training Consultancy Projects Products Semantic Technology • Assesment • Architectures • Modeling • Validation • Standard compliancy • End-to-end projects • mixed teams • research projects • EU framework • Unique Training Offer • Introduction • Modeling • Programming and manyothers…

  12. How to survive the document & data tsunami? Semantic Technology • State-of-the-art • Examples • Future

  13. Semantic Technology Solutions The ‘semantic web’ is an application of semantic technology Corporate solutions built with semantic technology include: • Knowledge Bases • Automatic Categorization & Archiving • Natural Language Processing in documents • …

  14. Semantic Technology SolutionsTenForce projects • Publications Office of the EU – a thesaurus of European activities • Wolters Kluwer Globally – building a multilingual publishing bus • DG Employment of the EC – a taxonomy of European Skills, Competences & Occupations

  15. Semantic Technology SolutionsAdvanced examples • New York Times – automatic categorization & archiving with Linked Data • Amdocs – telecom solutions for pro-active decision support • Audi – modeling behaviour to make testing less error-prone

  16. How to survive the document & data tsunami? Semantic Technology • State-of-the-art • Examples • Future

  17. Industry Analysts Gartner: high benefit rating (2010) “ Semantic technologies offer … options that now are difficult or impossible “ HP: top 10 trend in BI (2010) “New approaches are needed, and semantic technologies hold part of the solution.”

  18. A vision of the data web LOD2 – a European FP7 project • Build the infrastructure for the web of data • Opportunities & challenges for all of us!

  19. Future We know the tsunami is coming, the question is – who will be ready to survive?

  20. www.tenforce.com lambda.verdonckt@tenforce.com twitter.com/LambdaVerdonckt

  21. Back-up slides

  22. Semantic Technology SolutionsKnowledge Bases • Knowledge is captured in a model, making the DB a KB • Allows to manage & share knowledge i.s.o. mere storage >50% of companies indicate the need to share stored knowledge (VALUE-IT) • Better & faster retrieval of information for decision support • Human-readable: typical CRM with search functionality Machine-readable: expert systems, incl. reasoning eg. clinical decision support • Rules are part of the data, i.s.o. hard-coded: more readily adaptable to changing needs, while interoperable with existing DB’s

  23. Semantic Technology SolutionsAutomatic Categorization & Archiving Categorization based on controlled vocabularies (taxonomies, thesauri, ontologies) • makes content more searchable: better! • eliminates cost of labour-intensive processes: cheaper! vs. user-driven categorization & tagging (web 2.0) Remark: Look at Evrias an online example!

  24. Semantic Technology SolutionsNatural Language Processing Software that analyzes the structure and meaning of textual information • analyze texts, • identify terms & concepts, • extract information, • understand meaning • Automatic categorization & archiving based on NLP Tools: Alchemy, OpenCalais, PoolParty

  25. Wolters Kluwer Global Multilingual publishing system in a EU context for Legal, Tax & Regulatory TenForce

  26. DG Employment of the EU Commission ESCO, a taxonomy of European Skills, Competences & Occupations TenForce

  27. DG Employment of the EU Commission A Semantic Job Portal to leverage the information in ESCO and other information on the web TenForce

  28. Advanced examplesPublishing New York Times • in-house developed vocabulary • automatic categorization & archiving • published as Linked Data (open to the world!) http://data.nytimes.com/

  29. Advanced examplesTelecom RDF Amdocs Knowing why a customer is calling, saves 3’ per call (or € 0,30)! call center logs billing Pro-active decision support social fora ... advanced inference

  30. Advanced examplesManufacturing Audi (Ontoprise) Testing electronic systems in cars using simulations • huge amounts of data are recorded • to be collected and analyzed • time-consuming & error-prone Need for a standardized way to describe • desired system behaviour • known error-cases Solution: ontology-driven & visualized

More Related