IDOL presentation

© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

HP IDOLSpeaker’s name

Month day, 2014

© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.2

Video surveillance

Wire tapping

Internet of ThingsFacebook likes

Tweets

DronesOnline shopping Search queries Tweets

RBMS Social sentiment

CRM

Web logs

User clickstreams

Business data feedsMobile

SMS/MMS

User generated content

Apps YouTube

Service logs

The dawn of the information era

http://www.washingtonpost.com/business/technology/xbox-one-size-fits-all--post-buzz/2013/05/28/f7faa302-c7ca-11e2-8da7-d274bc611a47_video.html

http://www.washingtonpost.com/business/technology/xbox-one-size-fits-all--post-buzz/2013/05/28/f7faa302-c7ca-11e2-8da7-d274bc611a47_video.html


Improve customer relationship

Extend life expectancy

Deliver better, smarter products

Ensure governance & compliance

Protect and save lives

HP IDOL makes data matter



Understanding meaning is the key to solving information challenges

Risk modeling Fraud detection Competitive advantage Behavior analysis Knowledge delivery

?

Volume VelocityVariety Veracity

Fin Services ManufacturingLife Sciences Hospitality GovernmentTelecom RetailEntertainment Energy HealthcareMedia

Future challenges


Understanding human information

• Access and understand virtually any source of information on-premise and in the cloud

• A strategic pillar of HP’s HAVEn Big Data platform

• Non-disruptive, manage-in-place approach complements any organization

Social Media Video Audio Email Texts Mobile

Transactional Data IT/OTDocuments Search Engine Images

Harnessing the power


86%of corporations cannot deliver the right information, at the

right time, to support enterprise outcomes all of the time³

³Source: Coleman Parkes Survey November 2012

Keyword, metatags, database technologies often fail

Legacy technologies fall short

• Manual process does not scale

• Multiple definitions of the same word

• Not real-time

• Inaccurate and subjective

• Limited definitions, no relativity

• No idea distancing

• Interoperability of tagging

• Retroactive reporting


How does HP Autonomy approach human information?

Continuous learning based on incoming data and contextAdaptive

Mathematical, language independent technologyProbabilistic

Extract main concepts present in informationConcept

Combination of proprietary technology and proven industry standard methodologiesModeling


HP IDOL: Key enabling technology

• Mathematically based

• 15 years and over $280M in R&D

• >170 Patents

• Language independent

• Built for infrastructure

• All file types, all media types (voice/video)

• Scalable and with security

• Platform/OS /device agnostic

• Managed in place


Powered by IDOL, the OS for human information

Social Media Video Audio Email Texts Mobile TransactionalData

Documents IT/OT Search Engine Images

Apps for Exploratory Information Analytics

Apps for Information Governance and Management

Apps for Marketing Optimization

HP Autonomy connectors

Developer/Partner

External/CloudHP Autonomy Enterprise

Applications

The OS for human information

Repositories

Information types

OS service layers 500+ functions

DigitalSafe SharePoint Hadoop

CRM

Jive

Exchange

Relational DB

ACA AeD

WorkSite HP Records Mgr MediaBin

Data Protector Connected LiveVault

Driven by advanced analytics to understand data in context from any source

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=jG3mv_TI7NpzwM&tbnid=iN7L2G4LTCNgLM:&ved=0CAUQjRw&url=https://itunes.apple.com/us/app/salesforce-mobile/id281826146?mt=8&ei=gT2uUYbFKOeOiALkoYCICA&bvm=bv.47380653,d.cGE&psig=AFQjCNGfIxs5Oh83SXl3FQi87Z3LKQ_yxg&ust=1370459891742010

https://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=jG3mv_TI7NpzwM&tbnid=iN7L2G4LTCNgLM:&ved=0CAUQjRw&url=https://itunes.apple.com/us/app/salesforce-mobile/id281826146?mt=8&ei=gT2uUYbFKOeOiALkoYCICA&bvm=bv.47380653,d.cGE&psig=AFQjCNGfIxs5Oh83SXl3FQi87Z3LKQ_yxg&ust=1370459891742010


Over 500 IDOL functions to augment your intelligence

Automatic hyperlinking

Conceptual search

Keyword search

Fieldtext search

Phrase search

Phonetic search

Field modulation

Fuzzy matching

Implicit profiling

Explicit profiling

Community and expertise network

Agents

Intent-based ranking

Alerting

Social feedback

Eduction

Automatic clustering

Clustering 2D/3D

Autoclassification

Auto language detection

Sentiment analysis

Automatic taxonomy generation

Automatic query guidance

Highlighting

Parametric refinement

Summarization

Real-time predictive query

Metadata extraction

Automatic tagging

Faceted navigation

InquireSearch your data

InvestigateAnalyze your data

InteractPersonalize your data

ImproveEnhance your data


Search your data

• Conceptual, Keyword or Object• Extensive Field combinations• Full Meta Search

• Linearly Scalable• Fault Tolerant• Disaster Recovery Friendly

• All Information • Real-Time Data• Audio and Video

• Mapped Security• Fully Extendable• Leverages Existing Security

Accuracy

Robust Architecture

Reach

Security

Inquire“Search your data”

Investigate“Analyze your

data”

Interact“Personalize your

data”

Improve“Enhance your

data”




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data”Analyze your data

Quickly evaluate the relevance of information

• Automatic Query Guidance (providing top themes from query results in real time)

• Concept navigation via advanced visualizations (node graphs, theme tracking, topic maps, broadcast analysis)

• Intelligent summarization (simple, concept and context)

• Intelligent highlighting (search terms, phrases, concepts, context, fidelity to query grammar)

• Concept streaming (Real-time summaries from audio that are contextual to queries and intent)

• Intelligent de-duplication, including “near” de-duplication

Use structure to navigate the data

• Structured, semi-structured and XML support

• Parametric search (unlimited nesting and association support)

• Directed navigation (create compelling navigation for users)


Personalize your dataWe are what we…



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Personalize your data

Explicit profiling (agent): user-defined •Define your interest using:

- Natural language descriptions

- Keyword/ Boolean rules

- Refine by example

•Automatically monitor information

•Customizable

•Share interests with knowledge community

Implicit profiling: capturing behavior data• Fully automatic• Ongoing monitoring of data consumption

and contribution• Multi-faceted profiles• Always up-to-date

Expertise

CommunitiesAgents

Profiles

Dynamic communities of interest•Expert identification

•Define business rules to guide relationships

•Automatically form and manage community

•Collaboration Networks

•Document rating

•Consumer groups

Expertise Expertise



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Exploratory analytics that help you discover the “unknown unknowns”

Enhance your data

Managed classification• Create categories using business rules or training

Automatic classification and clustering• Automatically determine categories based on patterns and relationships in information• Spot analysis of all themes and grouping• Time sensitive analysis; What’s hot? What’s New?

Eduction• Apply structure to unstructured data by extracting key fields and entities• Hundreds of entities supported, including names, addresses, credit card information, sentiment, intent, etc

Audio analysis• Speaker independent speech to text, speaker identification, audio events, language identification, etc

Image and video analysis• Next generation image classification (is this a car?/find more like “this”)• On-screen OCR, logo detection, intelligent scene analysis, Color and texture analysis,

story segmentation, etc


HP Autonomy solutions family, powered by IDOL

IDOL

Compliance

Litigation Readiness

Storage OptimizationDatabase Archiving

eDiscovery

Supervision

Legal Hold

Enterprise Search & AnalyticsVoice of the Customer

Voice of the Worker

Media Intelligence

Video Surveillance

Big Data Analytics

Knowledge Mgmt

Content Access& Extraction

Records Mgmt

Legal Content Mgmt

Business Process Mgmt

Document Mgmt

Records Mgmt

Legacy Clean Up

Server Data Protection

Virtual Machine Data Protection

Remote & BranchOffice Data Protection

Endpoint DeviceData Protection

Cloud Data Protection

EnterpriseContent Mgmt

Archiving & eDiscovery

DataProtection

Web Experience Mgmt

Web Optimization

Search Engine Marketing

Marketing Analytics

Contact Center Mgmt

Rich Media Mgmt

Aurasma - Augmented Reality Mobile Experience

Digital Marketing Experience

Information Analytics

Information Management & Governance Marketing Optimization

Hybrid

OEM

Software

Cloud for human information


HP technology powered by IDOL

Enterprise Group

HP StoreAllHP StoreOnceHP Gen 8 Appliances

Enterprise Services

HP Social Command CenterHP Information Governance

PPS

HP FlowHP Live PhotoHP Connected Backup

IDOL + HadoopIDOL + VerticaHAVEn

Big Data

IDOL + ArcSight

Security HP Labs

Compass


Foundational methodology


Strong information and weak informationKey Words are small amounts of very strong information without contextLarger amounts of weaker information is what humans refer to as “context”

“Mercury”

Is it a planet?Is it an element?Is it a car?With high certainty; its and element!

“A heavy element and the only metal that is liquid at standard conditions for temperature and pressure with the symbol Hg and atomic number 80, commonly

known as quicksilver”


Uses pattern-matching and probabilistic modeling to form an understanding of content

HP IDOL understands the meaning of information

Fundamentally language-independent• Treats words as symbols

Allows incoming data to dictate the model, not pre-defined rules or dictionaries• Adapts to changing definitions

Optimized with language packs• Eduction, sentiment analysis, speech analytics

Information Theoryand Bayesian Inference


Best-in-class combination of approaches

XML and Boolean+

Natural language processing

Probabilistic



If we toss a coin 100 times and get heads every time, what’s the probability of getting a head on the 101st?

Traditional probability says: 50%© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.


If we toss a coin 100 times and get heads every time, what’s the probability of getting a head on the 101st?

Adaptive intelligence: prior information changes the model of understanding

Bayesian Inference says: 99+%© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.


What is in front of this wake?...

With high probability we can say there is a……..

BOAT!

© Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.25 © Copyright 2013 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.


Let’s play hang man

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Supercalifragilisticexpialidocious_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _


Platform features


Language independence

• Free from linguistic restraints and rules

• Automatically adapts to changing definitions

• Over 170 live customer languages

• Single, multibyte and Unicode languages

• Optional language packs for localization

Department of Homeland Security - Requires extremely precise handling of foreign languages, including Chinese and Arabic

Open V – China’s largest online video website


HP IDOL powers the largest systems in the world

Scalability

Millions of users

• Dept of Defense: 2.5 million users

Billions of documents

• Large bank: Over 1 bn emails

• Pharma: 50 terabytes of data in discovery repository alone

High throughput

• Bloomberg: Alert on 46m emails per day


Mapped security

• Fully integrated Kerberos authentication together with Secure Socket Layer (SSL) encryption across all transactions

• Compliance with all major Security Standards, including US DoD5015.2, UK TNA2002, Australia’s VERS, ISO 15489

• Full-range of customizable security functionality:

– Discretionary access control (ACL based)

– Mandatory access control (Based on metadata)

– Kerberized access to IDOL

– SSO authentication using Windows Active Directory

Single supplier to US Department of Homeland Security


Intelligent compaction

• Pause and resume the operation without causing corruption

• Monitor the progress

• Skip large sections of the index when appropriate to expedite the operation



http://host:ACIPort/action=admin

HP IDOL admin

Answer common questions and ease common actions:

• “Why is this query slow?”

• “What’s using up so much memory in my engine?”

• “Is my engine operating as expected?”

• “I need to perform some light maintenance (DREREPLACEs, etc) but don’t want to bother writing a perl script.”


Architecture


HP IDOL connector overview

Connector actions

• Synchronize (fetch)

• View

• Identifiers, Collect, Hold, ReleaseHold

• Insert, Delete, Update

Repository ConnectorConnector framework

serverIDOL

LUA w/IDOL extensions

DocumentFormat

detection

Pre-import processing

KeyViewfiltering

Post-import processing

LUA w/IDOL extensions

Index into IDOL

Repository

Connector

Connector framework server

IDOL

Repository

Connector

Repository

Connector

DIH

IDOL IDOL


HP IDOL data ingestion pipeline

LUA scripting engine is available within connectors

KeyView file format process, Eduction and LUA scripting engine are available within CFS

Repository

Connector

Connector framework server

Content

Repository

Connector

Repository

Connector

DIH

IDOL ProxyIndex tasks

OCR

Audio/Video

Category

APA Agents


Provides a flexible way of batching, scheduling, routing, and aggregating information into IDOL servers

Distributed Index Handler (DIH)

Features• Consistent hashing

• Batch indexing

• Index routing

• Virtual databases

• Categorization-based indexing

• Time-based indexing

Benefits• Seamless integration with backend modules

• Resilience

• Scalability

• Flexibility

IDOL Server 1 IDOL Server 2

Distributed Index Handler

(DIH)

Connector

Mirror/non mirror index


Intelligent query distribution

Distributed Action Handler (DAH)

Features

• Arbitrary distribution

• Mirrored configuration

• Non-mirror configuration

• Load balancing

• Fail-over

Benefits

• Linear scaling

• Improved performance

• Reduced processing time

• Robustness

IDOL 1 IDOL 2 IDOL 3 IDOL 4

1

DAH1

N 1 N

DIH1

N N

DAH3 DAH2

DIH3 DIH2


Globally distributed system


Advanced functions in depth


HP IDOL retrieval methods

Conceptual

• Natural language

• Conceptual matching

• Unstructured refinement

Business rules

• Boolean

• Keyword

Parametric

• Structured refinement



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Over 100 operators for Boolean search

AND

OR

NOT

NEAR

NEARn

DNEAR

DNEARn

WNEAR

WNEARn

BEFORE

AFTER

EOR

WHEN

WHENn

vAND

vSUBSTRING

vMATCHES

NEAR

NEAR/n

SENTENCE

PARAGRAPH

BEFORE

AFTER

ORDER

SOUNDEX

MANY

[n] WORD

CASE

PHRASE

. >

. >=

. <

. <=

. !=

. =

LANG/x

TODAY

YESTERDAY

NOW

NOW+n

NOW-n

term

term*

term?

vOR

vNOT

vACCRUE

vANY

vALL

vIN

vWHEN

vCONTAINS

vENDS

vSTARTS

vSUBSTRING

vCONTAINS

vENDS

vSTARTS

FREETEXT

STEM

TYPO

TYPO/n

YES-NO

PRODUCT

SUM

COMPLEMENT

LOGSUM

LOGSUM/n

MULT

MULT/n

FREQ

term~

term[100]

term[*1.5]

"term"

"term phrase"

term:field

"term

phrase":field

~term

FUZZY()

FUZZYnn()

SOUNDEX()

APCMMOD[]

term[~]



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Conceptual search

High recall and precision

• Return documents that do not contain query terms but are conceptually related

Input sentences or entire document as query

• Extracts main concepts in the query to deliver the most relevant results



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Automatic hyperlinking

• Automatically retrieves conceptually related content

• Searches automatically done for the user

• Increase productivity and reduce duplicate work



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Add context to short queries by grouping results into concepts

Automatic query guidance

Query ”Madonna”

Results: Documents containing ”Madonna”

Query search

Documents about:1. Singer2. Italian Renaissance3. Madonna Further

suggestions…

Most likely meaning…

Result documents

Conceptual clustering


Summarization

Quick summary(N+ lines)

Context summary(What is this doc about with relation

to query terms?)

Concept Summary(What do I look for with regards

to interest rates?)



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Information Theory andBayesian Inference


Directed navigation Inquire“Search your data”


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Narrow search with facets


Visualization of main topics Inquire“Search your data”


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Understanding the customer at the level of a dialog

Contextual segmentation

Geo + Demo + Psychographicsegments Behavioral segments

Functions

Performance

Feature Driven

Reviews

News

Adverts

Social media

Buzz driven

18-35 yrs

35-65

Seniors

Have Kids

Male

Female

Semantic segments

Large Screen

Lots of storage

High Res Display

Would give it 5 stars

Great Value for Price



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Intent-based ranking

Search results personalized and targeted based on user and context

Profile developed through complete behavior analysis… implicit or explicit profiling

Gather data from content consumption,

content contribution, interaction with colleagues, etc.



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Foster collaboration by automatically matching and connecting employees with similar needs

Connect with your colleagues

Experts

Communities

Files

Social



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Product performance issues

Clustering

Side letters

Off balancesheet transactionsAutomatically

partition the data so that similar information is clustered together



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Topical sentiment analysis

Decomposition and classification within a sentence to pull out specific topics

“I stayed at the Marriott last week, and though the mattresses were very nice, the service was awful.”

Is this Positive? Negative? Neutral?

How much Positive? How much Negative?



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Hundreds of conceptual entities

Eduction

Quickly narrow search results with auto-identified facets and conceptual entities such as employee names from documents

Validate or customize entities

• Is this a valid credit card number?

• What are all docs that contain SSNs?

• If area code is 415, output as Home Office

Pinpoint accuracy for multibyte languages such as CJK, Thai and some European languages

NamesPlacesIP addressesCompaniesEventsRelationshipsMedicinesAirportsCarsSocial Security numbersPhone numbersCredit cardsDatesHolidaysJob titlesCurrencies… many more



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Eduction Inquire“Search your data”


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<Organization>• National Security Agency

<Names>• President Obama• Vladimir Putin• Edward Snowden

<Places>• Moscow• St. Petersburg• Washington• Syria• Russia


Search video as easily as textTransform rich media into intelligent assets



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Live video or playback from archived footage

On-screen text recognition

Face identification

Automatically generated transcript using speech

recognition

Speaker identification

Timecodesynchronization

Automatic keyframe generation

AutomateAutomatically create metadata, keyframes, transcriptions

UnderstandUnderstand video footage and audio streams in real time

ActApply advanced analytics such as clustering and categorization, and link with other file types


Most advanced speech technology

Convert spoken words to text• Acoustic + Language Model

• Speech-to-Text and IDOL’s conceptual understanding

Eliminate manually adding metadata to A/V clips

Phonetic approaches have major problems• No Conceptual or Contextual Language Understanding

• Keyword-Based

Model of language disambiguates similar terms• U.S. President “Bush”

• “bush” as in a large plant



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Limitations of phonetic search

Phonetic sounds do not have a unique match

Only capable of keyword matching• “Cambridge University”• /k ey m b r ih jh y uw n ih v er s ax t iy/• The University of Cambridge• Cambridge colleges• Kings College• Trinity Hall

/k/ /ae/ /t/

“cat”

“category”

“scatty”

“catalogue”

?



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Accurate speech technology

Language independent, statistical algorithms to recognize speech +

Language dependent, acoustic and language models for each supported language

How is the voice being recorded?Telephone models, Hz Rates Broadcast Models

What language +common phases,product names, etc.

Trained dictionarywith vocabulary and conceptual

understanding

Recognized hypothesis



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Front end processing





Speech


Statistical models of speech and language

Speech-to-text technology

P(W) = probability of word string W

P(A|W) = probability of a acoustic sequence A given W

Use Bayes rule to find the word string w that has the highest probability given the acoustic sequence



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Language model

W = arg max P(W|A) = arg max P(W) P(A|W)P(A)

Acoustic model


Language model provides probability of word sequence

Forms a conceptual understanding of language

“Can I help you?” vs. “Can eye help you?”

Trained from large text corpora (Hundreds of millions of words)

Defines words that can be recognized

Use training text, e.g., broadcast news

Encompasses topic information, colloquial phrases, etc.

Adaptable for particular customer

Specialist vocabulary, e.g., product names



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Acoustic model analyzes the sounds that comprise a spoken language



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Audio analyzed to extract energy at various frequencies

Dependent on audio format

Complex statistical techniques model both the sounds and audio characteristics


Image technology: Text

Document field extraction



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<item><price>$6.23</price><date>10/2/2012</date><purpose>Lunch</purpose>…</item>

OCR: Read text from images

1D and 2D barcode reading

ISBN (“9870140189865”) PDF-417 (“LASTNAME, FIRSTNAME,…”)

Data Matrix (“The Future of Ticketing…”)

Many more (about 20 barcode types)

Image artifacts such as wrinkled paper

Avoid non-text parts of the image

Column understanding


Image technology: 2D objects

Registered image Test image

Generic Logo recognition

Registered Logos

Test image



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Image technology: Human analysis Inquire“Search your data”


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Primary clothing color = whiteNot nude

Primary clothing color = whiteNot nude

Primary clothing color = blackNot nude

Face detection

Face analysis

Found “President Obama” face


Hadoop/HDFS connector

Ingest Hadoop data into IDOL for advanced retrieval

Extract metadata, enrich and conduct advanced analytics for files stored in Hadoop

Push enterprise documents into Hadoop (chat data, ODBC, documents) for MapReduce analysis

Collect documents in Hadoop for legal collection


What’s new


HP IDOL 10

Extending leadership in human information analytics

More powerful Easier to operate Reliable

• Analyze sentiment at a granular level• Automatically extract 100s of entities

for improved search• Enhance your Hadoop investment• Deliver search results personalized for

each user• Improve audio and image analysis• Increase query speed by up to 30%

• Quickly answer performance-relatedquestions with our new visual dashboard, IDOL Admin

• Dynamically expand capacity without re-indexing for improved performance and no downtime

• Increase your indexing speed by as much as 47x with improved data transmission

• Recover intelligently from system failures with improved self-diagnosis of indices

• Securely delete content from your index

• Prevent the loss of documents during the indexing process


Latest innovations in IDOL 10

Core IDOL algorithm enhancements• Improved compaction

• Improved ability to repair indices

• Improved query speed

• Incremental backup and point-in-time restore

IDOL architecture improvements• Indexing flow control

• IDOL Admin

Speech• Multi-CPU support

Eduction• Improved handling of multi-byte

languages

• New grammars

• Degrees of sentiment analysis

• 3x performance improvement in sentiment analysis

Image• Object detection

• Unified analysis

…and many more!


Key strategic themes of HP IDOL development

Platform for search based applications

Enable internal and external partners to more easily leverage IDOL as a platform to build applications

Strengthen core functionality

Improve existing areas (e.g., sentiment), and continue growing in new areas (e.g., image)

Simplified consumption

Easier to install with more robust features

Consumable from private and public cloud for rapid web services

Next-generation enterprise search

Reinvent enterprise search in the era of cloud, mobile, and social computing

Big Data / analytics

Enable IDOL as content analytics platform in the broader Big Data / Information Analytics ecosystem; integrate Hadoop


Use cases


Insert slides from relevant decks


Thank you

IDOL presentation

Data & Analytics

Transcript of IDOL presentation