ai data model · professor paul morrissey, global ambassador; head of the data analytics & cx...

© 2018 TM Forum | 1

AI Data Model

Professor Paul Morrissey, Global Ambassador; Head of the Data Analytics & CX Group, TM Forum

Atul Ruparelia Data Architect Vodafone Group Services

Anil Maharjan, Senior BI Engineer, BSS / ITSS, Ncell/Axiata

Parveen Bhutiani, Senior Manager, Cognizant

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Professor Paul Morrissey, C.Eng, F.I.E.T.

Are Existing Telco Data Models Appropriate for AI?

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• Some Guiding Thoughts

• What the Analysts Say

• The Data Model Debate

• Machine Intelligence

• The Case for the Defense

• Narrative

• Knowledge Graphs

• The Case for the Prosecution

• Best Practice

• Next Steps, Outcomes & Food for Thought

• Questions

Agenda

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“Putting together a strategy for AI will be

on everyone’s agenda for 2018.”

“Big data and AI for business are on the

rise big time. Overall business spend on AI will increase by 300%in 2018, compared to

2017.”

“Artificial intelligence has disruptive and

transformative impact on CSPs.”

Information Security Level 2 – Sensitive© 2017 – Proprietary & Confidential Information of Amdocs6

What the analysts say…

“AI could potentially deliver additional

economic output of around $13 trillion by 2030, boosting global

GDP by about 1.2 percent a year ”

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Machine Intelligence

Machine ReasoningMachine Learning

Learned models providing insightsand actions for particular problems

Logic conclusions from assertedknowledge in semantic world models

Examples: regression models, random forests, deep neural

networks, reinforcement learning, …

Examples: Logic reasoners, Prolog, modelling languages, OWL, CYC,

…

Use Cases: Find optimal solutions for specific problems.

Use Cases: reason about the meaning of insights within a broader

context

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Are we measuring the right things OKR’s

OKR’s

John Doerr

https://www.ted.com/talks/john_doerr_why_the_secret_to_success_is_setting_the_right_goals

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The AI Data Model

AI will rely on Knowledge Graph & Semantic Data Models...The Case for the Defence….

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AI will rely on Graph Databases & Semantic Data Models...The Case for the Defence….

The idea is quite simple the overall goalof semantic data models is to capturemore meaning of data by usingrelational concepts with more powerfulabstraction concepts this enables theprovision of high level modellingprincipals as an integral part of a datamodel, in order to facilitate therepresentation of real world situations,rather than the logical data structure ofa database management system (DBMS)

Semantic models are fact-oriented (asopposed to object-oriented). There aremany benefits to the Semantic Graph(SG) database approach. Perhaps themost unique is the ability to infer orunderstand the meaning ofinformation. With complex datasemantic technology we can link newinformation automatically, withoutmanual user intervention or thedatabase being explicitly pre-structured

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Comparisons

RelationshipSchema

Maintenance

Query

LanguageIndexing Maturity

Optional

RelationshipECO System Storage

RDBConvey meaning

through table namesRigid Schema

Industry Standard

SQL

Indexing can be

complicated critical

to scaling

Well established

30+ yearsCan be challenging

Very rich

development with

lots of tools

available

Efficient use of

space

NoSQL NoData is structured

but highly simpleNone

Indexing can we

complicated critical

to scaling

Maturing 10+ years NoneRich development,

limited tools

Document Stores

(Key-Value Pairs)

Semantic Graph

Database

Covey meaning in

their self definition

None -Relationship

coveys meaning of

definition

Industry standard

SPARQL

Indexing can be set

as automaticRecent 5+ years

Optional, can be

enforced

Maturing ecosystem

and tools

Single Standard

schema (triplet)

The Relationship Triplet More Triplets Questions

Q: Does anyone dream?

o A: Josh and Greg

Q: What do people dream about?

o A: Football and Traveling

Q: Do Greg and Josh have anything in common?

o A: They are interested in Football,

and they are interested travelling

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Knowledge Builds Quickly

In the diagram, we can easily query orretrieve the following information:

What Carl likes to do, who is his brother and what sport does his colleague like to play?

This ecosystem gets enriched every time you enter data, because it is connected to a huge network

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Operationalization

Therefore, with more data, there willbe more context and more potentialinsights. Furthermore, thiscontextual value grows exponentiallylike a network and networks aregraphs. This is where the context isderived from. Context compresses allthe information (person info, whathe/she wants to retrieve, his/herprevious activities and his/herinterests) about anything, i.e. aperson, a place, an object.

The Consumer web is one of the bestexamples to understand the value ofcontext within data. A number ofcompanies are evolving, and they areimplementing knowledge graphs in theirsystems. Before implementation ofknowledge graphs, if you were to searchfor price of a product along with someother information such as sales,promotions, availability in your area, bestseller etc. You had to crawl somereferences offered against your searchstring in order to see all theresults/information.

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Search

But with knowledge graphs implementation, the application transforms the search from “strings” to “things”. And now against a search, you can get a link to the price of that product, but as application has learnt from your past activities, now it knows what actions you most likely want to take based on your context

So, it will also offer you where did you buy same product in the past, was there any promotion that time, is there any season end sales from some other stores etc. Google search clearly implements such knowledge graphs based on a SG database.

That is why we get structured and detailed information about the topic in addition to a list of links to the other sites. This procedure has allowed Google to focus its search on things or concepts and understand exactly what a user is looking for based on context. We need to start thinking about this type of functionality in the Service Provider domain where the amount of data that is held is a veritable gold mine of contextual information.

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Knowledge Graphs

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Where Next?Gartner states in its Hype Cycle for Artificial Intelligence, 2018: “The rising role of content and context for

delivering insights with AI technologies, as well as recent

knowledge graph offerings for AI applications have pulled Knowledge

Graphs to the surface.

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The AI Data Model.

AI will rely on Knowledge Graphs & Semantic Data Models...The Case for the Prosecution ….

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Next steps, Outcomes & “Food for Thought”

• Continue to Discuss in the Open Forum

• Produce a ‘White Paper’ of alternatives for 18.5

• Initiate some Ambidextrous experiments

• Create a Worry Budget (WMD) →→→→

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Questions?

Professor Paul MorrisseyGlobal Ambassador BDA & CX

AI Data Model Program Lead

[email protected]

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ai data model · professor paul morrissey, global ambassador; head of the data analytics & cx...

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