Copyright © 2014 Tapestry Solutions, Inc. All rights reserved. Role of Ontology in ‘Big Data’...

Copyright © 2014 Tapestry Solutions, Inc. All rights reserved.

Role of Ontologyin

‘Big Data’

Jens Pohl, PhD

Monday 28 July, 2014

TAPESTRY / MIRO – PROPRIETARY

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc.All rights reserved.

TAPESTRY / MIRO – PROPRIETARY 2

Origins of Big DataBig Data & Ontologies

Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

By-product of the evolution of larger and more complex societal structures.

Result of the exponential increase in data due to global connectivity.

Big Data is not a completely new phenomenon.



Planning is a Critical ToolBig Data & Ontologies

Expectation that the plans will be effective.

Decisions must be made in a timely manner.

Forecasts must be at least reasonably accurate.

Organizational complexity generates a need for efficiency through planning.




Planning is PredictiveBig Data & Ontologies

Plans are based on assumptions.

Assumptions are predictive in nature.

Forecasting future conditions and events based on past experience is problematic.

Planning and forecasting are closely related.




Forecasts are Mostly WrongBig Data & Ontologies

Western Union Exective – 1876:

"The telephone has too many shortcomings to be seriously considered as a means of communication."

Lord Kelvin – 1895:

"Heavier-than-air flying machines are not possible."

Thomas Watson, IBM Chairman – 1943:

"I think in the world there is a market for maybe five computers."

Ken Olson – 1977:

"There is no reason for individuals to have a computer in their home."

Bill Gates – 1981:

"64.000 bytes of memory ought to be enough for anybody."

Robert Metcalfe (inventor of the Ethernet):

"The Internet will catastrophically collapse in 1996."




Early Data Analysis ProblemsBig Data & Ontologies

We rely largely on the analysis of past events to identify future trends.

Periodic collection of population census data (every 10 years in the US).

Collection of data is time consuming, but the analysis of the data is even more onerous.

The 1880 US census took 8 years to process.


Traditional Big Data Analysis

InferentialStatistics

Collection of very small sample.

Representativeness ensured by randomness.

Mathematical analysis of sample.

Predictions about entire corpus of data.

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4

TraditionalBig Data Analysis

Hypothesis based on theory(s).

Collection of represen-tative data sample.

Correlation analysis of random sample.

Testing of hypothesis (and data).

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Big Data & Ontologies



TAPESTRY / MIRO – PROPRIETARY

.

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New Big Data Analysis ApproachBig Data & Ontologies


Correlation: If A occurs with B then we can predict that A is likely to occur wherever B occurs; - i.e., B is a proxy for A.

The analysis is based on a data set that is essentially equivalent to the entire corpus of data.

Assumption: Any data domain changes are gradual and not abrupt.

Assumption: The corpus of data is continuously extended with new domain data.

The correlation is a probabilistic likelihood and not an absolute certainty.


Blind correlation through

brute force computation

Massive Data

Automatedextraction of

meaning

What!

Particular Knowledge

Why!









From Data to Knowledge

LOW VOLUME HIGH VALUE

HIGH VOLUME LOW VALUE

KNOWLEDGE(INTERPRETATIONS AND RULES)

INFORMATION(RICH IN RELATIONSHIPS)

PURPOSEFULDATA

(ORGANIZED)

LOW LEVEL DATA(UNORGANIZED)





Wasteful Use of Human Resources

Human computer user must interpret and manipulate data by adding context..

Context

Data without context cannot be automatically

interpreted by computers

Knowledge

Information

Organizedand

UnorganizedData





Fundamental Distinctions

rain airport 6 to dense clear for not Scotland in hours field 117 pilot expected 49 Glasgow railcar fog week 82

"…dense fog in Glasgow,

Scotland, not expected to clear for 6 hours…"

KNOWLEDGE comprises inferences derived from

information.

Aircraft bound for Glasgow International

Airport are likely to be rerouted or

delayed.

INFORMATION is numbers and words with relationships.

DATA are numbers and words without

relationships.





Principal Context Components

History

Time

Location

Environment

Identity

CultureUrgency

Activity

"...any information that characterizes the interaction of entities

(i.e., players and objects), within a given situation…"





Context as an Enabler

Context is a prerequisite for …

Automated interpretation of data.1

Automated filtering of data.2

Automated retrieval of useful data.3

Intelligent collaborative decision tools.4

Self-healing and secure information networks.5

Responsive human-computer interfaces.6





Virtual Model of Real World Context





Ontology Representation

Defines the innate nature and operational context within which the actual values of entities can be accurately interpreted.

Rich Relationships

Logic (Business Rules)

ModelingPatterns and Techniques

Provides: Semantic context.





Ontology Construction





Ontology of Real World Context





Human-Computer Partnership

Ontology provides context for automated reasoning by software agents.

Human Context

Computer Context

Organized Data

Information

Knowledge

Unorganized Data

Ontology

Data capture in context



Load - Planning from a Data ViewpointBig Data & Ontologies

The common parameters of Big Data are Volume, Velocity and Variety (3Vs).

Over 30,000 cargo items per ship.

From one load-plan in two days to four load-plans in two hours.

Over 300 attributes per cargo item, 320 ships, 250 aircraft configurations, and over 15,000 railcars.


Volume

Velocity

Variety

ICODES-GS v6: Overview



ICODES v6 Portal


Evolution of ICODES: 2011: Fielding of ICODES GS

Within a Collaborative Information Workspace (CIW), ICODES GS becomes a set of intelligent reusable services, with user-transparent data exchange capabilities, which are

accessed through a single sign-on portal.





ICODES GS: Applications and Services





ICODES-GS v6: SLP

Single Load Planner (SLP)



Single Load Planner (SLP)






ICODES - SLP: Knowledge Domains

The core of ICODES GS is its knowledge-base of context and business rules that allow agents to automatically interpret data changes and provide useful assistance to the operator.

ICODES knowledge domains include:

ICODES user-interfaces include:

ICODES: Ontology-Based Multi-Agent System

HazardAgent

HatchesAgent

DoorsAgent

OpeningsAgent

AccessAgent

RampsAgent

USER

MULTI-MEDIA

Trim & Stability Agent

LayeredOntology

CADEngine

CranesAgent





ICODES : Ship Load-Planning User-Interface

Tool BarsAgent Status

Bar

GraphicsWindow

StatusBar

Main Menu Bar

AssociationsToolbar





ICODES GS: Four Domains – Aircraft, Ship, Rail and Yards

Aircraft Ship

Rail Yards





ICODES-GS v6: DC

Data Cleanser (DC)

Data Cleanser (DC)




ICODES-GS v6: DC

A set of components that serve as the source of reference data for ICODES v6 and provides the operator with the capability to validate, correct, and automatically populate cargo data.

• Problem: Incorrect or partial user input.

• Solution: Validate and Auto-Fill using Ref. Data

• MARVEL AES-based solution

ICODES 6Reference

Data

Data Cleanser Service

ICODES 6 Applications & Services

reads reads

Data Cleanser (DC)




ICODES-GS v6: DCDC Web-Application




ICODES-GS v6: IR

Information Repository (IR)

Information Repository (IR)




ICODES-GS v6: IRInformation Repository (IR)

ICODES v6 Components

• Web Service

• Define and list categories of data

• Publish data to a category

• Remove data from a category

• Browse, search, and retrieve data residing in a category

• Standalone ApplicationProvides a user interface that enables an end-user to utilize the IR Web Service capabilities.

• Embeddable Components

Provides a unified software library that allows ICODES v6 applications to present standardized dialogs for import and export.

EnterpriseUsers

DesktopUsers

SLP

CB

IR StandaloneApplication

EIP Environment

SLP, CE,BBT, andCB

DBIR Service

ICODES v6 components that provide operators, services, and applications with a centralized location for sharing data in support of user collaboration.




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