Data aware apps
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What it takes to build data aware applications. Why Web Interoperability is broken?
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Data aware apps
- 1. Data-aware appsChallenges & Opportunities Tal Muskal – CTO Sagie Davidovich - VP R&D
- 3. APIs:
- 5. APIs Operate on the keyword level
- 6. Discovery doesn’t exist (WSDL failed)
- 7. Not aware of each other
- 8. No standard ontology / format
- 9. Manyof them
- 10. 99.9% of the information on the web is unstructured
- 11. Web data is partial, noisy, inaccurate, not objective
- 13. Abstraction stack - towards data aware apps Today
- 14. A typical data flow Input Process Output XML/ JSON Order Search Filter Transform Data aware UI components Interaction Aggregate Format Persist Sensors Generate Compute Augment Stream Trigger
- 15. Data aware web - Pragmatic approach Data aware app host WWW Semantic Abstraction layer Reasoner & data flow manager Query Enrichment APIs Result refinement Abstractions Library Synthesis Unstructured Web pages Semantic Store Entity & Fact extraction
- 16. Abstractions Libraryovercoming the complexity barrier Lunchsuggest = Meals(familynearby), Meals(friendsnearby) Meals(folks) = folks/foodtaste/common/nearByRestaurants/deals FamilyNearby= facebook:connections/filter(family, near) NearMe(object) = |object/location - me:location| < 10km location= sensor:gps family = children, spouse, parents, …
- 17. What we’ve done at SemantiNet High performance graph store (Patented) Common sense & world knowledge ontologyWikipedia & Linked-data (2Bn edges) Data query & reasoning engine (Patented)distributed,simple query language Semantic abstraction library over APIs & the webcollaborative, includes semantic query refinements A complete stack of NLP & data mining librariesfor querying the unstructured webFact Extraction & Ontology aware contextual disambiguation Templating enginefor data aware UI elements Web development environment for data aware appscollaborative, hosted, UI components library Cloud hosting for data aware apps.
- 18. Thank you More coverage on: semanticweb.com/build-data-aware-apps-without-the-hassle_b17465 blog.headup.com
Hinweis der Redaktion
- We’ve overcome the first two complexity barriers by introducing standard protocols, encoding and formats for representing information.The next step would be establishing an abstraction layer and standards for meaning.Once this is done, web APIs and data sources can exchange information on the meaning level, and do this transparently.Data driven applications are expressed as data flow graphs, and the problem of building these applications is reduced to a path finding problem.
- A typical data flow, starts from a set of inputs, goes through a transformation & processing flow, and then output as a machine readable, human readable format, persisted or used as a trigger for external applications.Sensors can be for example the user’s GPS coordinates.
- Personal email: mesagie [at] gmail [dot] com