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Slide 1 Slide 1 Presentation Transcript

  • Microsoft Semantic Engine
    Naveen Garg, Duncan Davenport
    Microsoft Corporation
    SVR32
  • Microsoft Semantic Engine
    Unified Search, Discovery and Insight
  • The Situation Today
    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 Solution
    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
  • Scenario|Meaning driven insight
  • SCENARIOS|UNIVERSAL APPEAL
    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
  • FEATURES|UNIFY YOUR CONTENT
    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
  • DEMO|VIEWS GALLERY
    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
  • DESIGN|MEANING-DRIVEN PROCESSING
    Streams | Descriptors (Properties) | Kinds (Concepts)
    Streams processed into contextualized and indexed concepts for search | discovery | organization
    LEGAL DOCUMENT
    CONCEPT
    EVIDENCE
    CONCEPT
    LEGAL CASE [xxx]
    CONCEPT CLUSTER
    KR_CLIENT_225.docx
    STREAM
    EXTRACTED PROPERTIES
    PROPERTY
    BILLABLE WORK
    CONCEPT
    DEPOSITION
    CONCEPT
    SEARCH AND SHARE
    MDP
  • Engine consists of self-contained set of pluggable services
    Search and Markup
    Trend and Predictive Analysis
    Automatic Organization
    Recommendation and Discovery
    Semantic Engine
    Clustering
    Text Processing
    Video Processing
    MDI (RBV)
    Image Processing
    Audio Processing
    Supervised Machine Learning
    Conceptual Search
    Inference
    Sequence Store
    (Suffix Tree)
    Distributed Content Store
    Ontology and Taxonomy Management
    DESIGN|ARCHITECTURE
  • 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
    Analysis3
    Analysis2
    Analysis1
    The logical architecture partitions analysis, indexing and storage
    Staging
    Core
    Index
    Stream
    Single Logical
    Partitionable
    DESIGN|SCALABLE ARCHITECTURE
  • Designed to be hassle free out of the box
    Several programming languages and frameworks supported
    CLR/.NET, JavaScript, TSQL, C++
    DESIGN|PROGRAMMING
  • DESIGN|PROGRAMMING
    Sample of storing a stream in the system
    Initiates the content processing, classification, and indexing
  • DESIGN|PROGRAMMING
    Sample of search and recommendations
    Returns contextual results from the store and the web
  • DEMO|WINDOWS 7 SHELL EXTENSION
    Seamless Integration in Windows Desktop Federated Search
    Expose Meaning-Driven Indexing and Semantic Actions
    Zero Learning Curve
  • 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
  • DESIGN|ANATOMY OF A KIND
  • DESIGN| MODELSPACE
  • 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
  • DEMO|SQL PROPERTY PROMOTION
    Seamless Integration of Meaning-Driven Indexing in ALL SQL Tables
    Expose Meaning-Driven Indexing via T-SQL
  • PARTING THOUGHTS
    Unified Search, Discovery and Insightover Every Digital Artifact
    Extensible and Scalable Semantic Platform
    Zero Learning Curve
  • YOUR FEEDBACK IS IMPORTANT TO US!
    Please fill out session evaluation forms online at
    MicrosoftPDC.com
  • 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….