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

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 3 Planning is a Critical Tool Big 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. Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 4 Planning is Predictive Big 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. Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 5 Forecasts are Mostly Wrong Big 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: " bytes of memory ought to be enough for anybody." Robert Metcalfe (inventor of the Ethernet): "The Internet will catastrophically collapse in 1996." Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 6 Early Data Analysis Problems Big 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. Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Traditional Big Data Analysis Inferential Statistics Collection of very small sample. Representativeness ensured by randomness. Mathematical analysis of sample. Predictions about entire corpus of data Traditional Big Data Analysis Hypothesis based on theory(s). Collection of represen- tative data sample. Correlation analysis of random sample. Testing of hypothesis (and data) Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 7 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY. 8 New Big Data Analysis Approach Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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. Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Different Computational Approaches Blind correlation through brute force computation Massive Data Automated extraction of meaning What! Particular Knowledge Why ! Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 9

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 10 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems From Data to Knowledge LOW VOLUME HIGH VALUE HIGH VOLUME LOW VALUE KNOWLEDGE (INTERPRETATIONS AND RULES) INFORMATION (RICH IN RELATIONSHIPS) PURPOSEFUL DATA (ORGANIZED) LOW LEVEL DATA (UNORGANIZED)

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 11 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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 Organized and Unorganized Data

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 12 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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.

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 13 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Principal Context Components History Time Location Environment Identity Culture Urgency Activity "...any information that characterizes the interaction of entities (i.e., players and objects), within a given situation…"

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 14 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 15 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Virtual Model of Real World Context

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 16 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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) Modeling Patterns and Techniques Provides: Semantic context.

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 17 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Ontology Construction

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 18 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Ontology of Real World Context

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 19 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 20 Load - Planning from a Data Viewpoint Big 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. Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems Volume Velocity Variety

ICODES-GS v6: Overview Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 21 ICODES v6 Portal Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

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. Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 22 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES GS: Applications and Services Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 23 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES-GS v6: SLP Single Load Planner (SLP) Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 24 Single Load Planner (SLP) Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 25 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems 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 Hazard Agent Hatches Agent Doors Agent Openings Agent Access Agent Ramps Agent USER MULTI- MEDIA Trim & Stability Agent Layered Ontology CAD Engine Cranes Agent Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 26 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES : Ship Load-Planning User-Interface Tool Bars Agent Status Bar Graphics Window Status Bar Main Menu Bar Associations Toolbar Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 27 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES GS: Four Domains – Aircraft, Ship, Rail and Yards AircraftShip RailYards Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 28 Big Data & Ontologies Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES-GS v6: DC Data Cleanser (DC) Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 29 Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

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 6 Reference Data Data Cleanser Service ICODES 6 Applications & Services reads Data Cleanser (DC) Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 30 Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES-GS v6: DC DC Web-Application Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 31 Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES-GS v6: IR Information Repository (IR) Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 32 Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems

ICODES-GS v6: IR Information 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 Application Provides 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. Enterprise Users Desktop Users SLP CB IR Standalone Application EIP Environment SLP, CE, BBT, and CB DB IR Service MARVEL AES offers inexact search over metadata. ICODES v6 components that provide operators, services, and applications with a centralized location for sharing data in support of user collaboration. Copyright © 2014 Tapestry Solutions, Inc. / Miro Technologies, Inc. All rights reserved. TAPESTRY / MIRO – PROPRIETARY 33 Global Services & Support | Training Systems and Government Services | Logistic Information Management Systems