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Microsoft Semantic Engine

SVR32. Microsoft Semantic Engine. Naveen Garg , Duncan Davenport Microsoft Corporation. Unified Search, Discovery and Insight. Microsoft Semantic Engine. Significant Content is Outside Structured Storage (RDBMS, OLAP, BI)

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Microsoft Semantic Engine

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  1. SVR32 Microsoft Semantic Engine Naveen Garg, Duncan Davenport Microsoft Corporation

  2. Unified Search, Discovery and Insight Microsoft Semantic Engine

  3. Significant Content is Outside Structured Storage (RDBMS, OLAP, BI) Integration of this Content is Prohibitively Expensive (Time, Money, Resources) Extracting Insight, Analytics, and Recommendations is even harder Situation is a Confluence of Search | Predictive Analytics | Large-Scale Collaborative Filtering The Situation Today

  4. Having all forms of digital information on asingle platform allows people to blend unstructured and structured content and to drive insight and decision making Microsoft Semantic Engine provides a combination of technologies to form a contextual understanding of all digital content The Solution

  5. Scenario|Meaning driven insight

  6. Search and Collaboration | Personalized search, discovery and organization Legal | Precedent and subject based search over large scale textual corpuses Life Sciences | Systems biology with large volume data correlation and search Government Services | Intelligence, real-time analytics, visualization, clustering Social Networking | Social graph relevance mining, ranking criteria auto tuning SCENARIOS|UNIVERSAL APPEAL

  7. Unified Search, Discovery and Insight Automatic Clustering and Organization Meaning-Driven Indexing, Classification and Storage Scalable Content Processing over all Content Types Instant On Experience for Out of Box Value FEATURES|UNIFY YOUR CONTENT

  8. Search, Discover and Organize features exposed via sample UX gallery Seamless installation and indexing of desktop, email and web content Fully documented Managed APIs used in UX gallery and JavaScript / C# samples DEMO|VIEWS GALLERY

  9. LEGAL DOCUMENT CONCEPT EVIDENCE CONCEPT LEGAL CASE [xxx] CONCEPT CLUSTER Streams | Descriptors (Properties) | Kinds (Concepts) Streams processed into contextualized and indexed concepts for search | discovery | organization KR_CLIENT_225.docx STREAM EXTRACTED PROPERTIES PROPERTY BILLABLE WORK CONCEPT DEPOSITION CONCEPT SEARCH AND SHARE MDP DESIGN|MEANING-DRIVEN PROCESSING

  10. Search and Markup Trend and Predictive Analysis Automatic Organization Recommendation and Discovery Semantic Engine Clustering Text Processing Video Processing MDI (RBV) Engine consists of self-contained set of pluggable services Image Processing Audio Processing Supervised Machine Learning Conceptual Search Inference Sequence Store (Suffix Tree) Distributed Content Store Ontology and Taxonomy Management DESIGN|ARCHITECTURE

  11. Scale out by adding boxes; standard “web farm” (VIP) configuration Scale out by adding boxes; each box can run all processors or specific processors Store(<content>) Annotate(<kind>) Index(<content>) Organize(<kinds>) Search(<query>) … Text Image Audio Video Video API1 API2 API3 Analysis1 Analysis2 Analysis3 The logical architecture partitions analysis, indexing and storage Staging Core Index Stream Single Logical Partitionable DESIGN|SCALABLE ARCHITECTURE

  12. Designed to be hassle free out of the box Several programming languages and frameworks supported CLR/.NET, JavaScript, TSQL, C++ DESIGN|PROGRAMMING

  13. Sample of storing a stream in the system Initiates the content processing, classification, and indexing DESIGN|PROGRAMMING

  14. Sample of search and recommendations Returns contextual results from the store and the web DESIGN|PROGRAMMING

  15. Seamless Integration in Windows Desktop Federated Search Expose Meaning-Driven Indexing and Semantic Actions Zero Learning Curve DEMO|WINDOWS 7 SHELL EXTENSION

  16. Files PlugIns Importers PlugIns Importers Plug-Ins Importers API Layer System Integration Fabric (SIF) KindLink Kind Descriptor Stream Semantic Engine Database DESIGN|ARCHITECTURE DETAILS ListKind

  17. DESIGN|ANATOMY OF A KIND

  18. DESIGN| MODELSPACE

  19. Periodically, MSE checks the User database for Changes All Change data is returned to MSE as one XML block MSE creates Kinds and Descriptors as needed, and Commits the activity MSE data is exposed through custom views keyed to the Users’ Primary Keys DESIGN| PROPERTYSPACE

  20. Seamless Integration of Meaning-Driven Indexing in ALL SQL Tables Expose Meaning-Driven Indexing via T-SQL DEMO|SQL PROPERTY PROMOTION

  21. Unified Search, Discovery and Insightover Every Digital Artifact Extensible and Scalable Semantic Platform Zero Learning Curve PARTING THOUGHTS

  22. YOUR FEEDBACK IS IMPORTANT TO US! Please fill out session evaluation forms online at MicrosoftPDC.com

  23. Learn More On Channel 9 • Expand your PDC experience through Channel 9. • Explore videos, hands-on labs, sample code and demos through the new Channel 9 training courses. channel9.msdn.com/learn Built by Developers for Developers….

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