C r e a t i n g I n t e l l i g e n t A p p sw i t h S e m a n t i c s & B i g D a t a

O c t o b e r 2 , 2 0 1 3

Marc C. Hadfield, FounderEmail: marc@vital.ai

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<introduction>Marc HadfieldFounder of Vital.AI

Vital.AIBig Data Application Platform:

Semantics & Big Data in one platform,

combining multiple Data Analysis

techniques.

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Intelligent Applications

Development Processes

Business Value

Today:

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Intelligent Applications:

...learn from experience.

Thus, are experience-driven: data-driven.

“self-optimizing”

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Data-Driven Applications

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Moderni.st

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Moderni.st

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Personal Agent App

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Why are data-driven applications different?

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User Interface

Business Logic

Database

Traditional Applications

Why are data-driven applications different?

“sta%c”

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Why are data-driven applications different?

“dynamic”

User Interface

Business Logic

Events

Data-Driven Applications

Data

Analytics

Derived Behavior

Logic

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Data-Driven Applications use many different technologies, with many different skill sets:greater power, higher cost.

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Why do we want a data-driven application?

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What are the components of a data-driven application?

Data Collection

Dynamically Adjust Behaviors

Data Analysis / Feedback

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Implementing a Data-Driven Application.

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Implementation Goals:

Off the shelf?

Minimize Impedance Mismatch:Mobile/Web User Interface vs.

Semantic vs.Big Data vs.

...

Efficient Development Processes

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Vital.AI Platform Stack

Clean separation of responsibilities and skill sets.

Rapid Development of Data-Driven Apps.

VitalSigns: Ontology-based Data Model.

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Vital Prime

REST Interface

Collects Real-Time Events

Database Interfaces:HBase, Allegrograph, ...

Workflow Interface

Script Engine

In-Memory Analytics

User State Management

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Vital Flows

Implements Workflows:

Natural Language Processing

Run Predictive Analytics

Graph Analytics

Logical Inference

Many open-source components in a common workflow framework.

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Hadoop

Implements Big Data Analysis:

Machine Learning

Build Predictive Models

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Vital Core Ontology

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Vital Core Ontology

Vital Domain Ontology

Application Domain Ontology

Extending the Ontology

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Generating Data Bindings with VitalSigns:

Ontology VitalSigns

Groovy Bindings

Semantic Bindings

Hadoop Bindings

Prolog Bindings

Graph Bindings

HBase Bindings

JavaScript Bindings

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person123.name = "John"person123.worksFor.company456

<person123> <hasName> "John"<worksFor123> <hasSource> <person123><worksFor123> <hasDestination> <company456><worksFor123> <hasType> <worksFor>

person123, Node:type=Person, Node:hasName="John"worksFor123, Edge:type=worksFor, Edge:hasSource=person123, Edge:hasDestination=company456

Groovy

RDF

HBase

Data Representations

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Editing the Ontology

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Developing with the Ontology in UI, Hadoop, NLP, Scripts, ...

Node:Person Node:PersonEdge:hasFriend

Set<Friend> person123.getFriends()

“Best Practices” in Ontology Development.

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Data Analysis

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Using Natural Language Processing

Topic Categorization Extract Entities

Dialogue System Entity Normalization

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Using Graph Analytics

PageRank, Centrality, Interest Graph, ...

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Using Machine Learning

Implemented via Hadoop

Algorithms from Mahout

Build Predictive Models

Models used in Workflows

Data defined in Ontology

Classification, Clustering...

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Data Visualization - Cytoscape

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Data Management - Dashboard, Wiki

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Examples

Shopping Recommendation App

Personal Agent App

Content Recommendation App

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Content Recommendation

REST Interface

Vital Client

Vital Prime

Vital Flow Queue

Integrator NLP HadoopML Model

S3

Elastic Map/Reduce

Mahout

300M Users

100K Publishers

NLP

Machine Learning

Content + User Signals

Recommendations

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Personal Agent

Dialogue System

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Personal Agent

Interest Graph Recommendations

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Moderni.st

App Server

Vital Prime

IntegratorFlow

NLP Flow Logger Flow

Inference Flow

VITAL APIVitalSigns

Web Browser /Mobile Device

HDFS

Hbase Hadoop JobsMahout

Vital Flows

Flume

Allegrograph

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Creating an Intelligent App

Create User Interface.Use Vital Client, Send Signals

Create App Ontology, includingDomain Classes & Events/Signals

Define Workflows for Data Processing

Define Predictive Models, Build Models

Create scripts for App Functionality

Deploy Components

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Development Processes

Use the right tool for the right job,

& the right people for the right job.

Integrated Framework.

Coordinate on Ontology.

Eliminate Data Mis-Matches.

Clean code separation of “paradigms.”

Solve “Variety” Big Data challenge.

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Business Value

Intelligent App.

Efficient, Rapid Development.

Flexibility to learn, adapt, improve.

Greatly reduce risk.

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For more information, please contact:Marc C. Hadfield, FounderE: marc@vital.aiP: 917.463.4776

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THANK YOU!

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