Semantic Meta Data Driven Enterprise v2 - OMG | Object ... 1November2013|) Proprietary)and)conﬁden4al)informaon.)©)2013)MphasiS ) ModernEnterpriseITEnvironment: Ecosystemofmetadrivensystems

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1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS 1

Mastering Enterprise Metadata with Seman2c Modeling

Enterprise Metadata: The descrip4on of the organiza4onal context – processes, roles, policies, products and offerings, etc. – that are implicitly part of the enterprise informa4on ecosystem. Seman2c Modeling: A data model linked to the real world through a conceptual model

If we combine enterprise metadata with an enterprise seman4c web ini4a4ve, we can create a knowledge fabric that can completely change the way we think of enterprise soPware

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Cust. ID Name Address Drivers License

The seman4c model is not just part of the metadata, it is the metadata

SID: ORCL

Seman2c Model: A data model linked to the real world through a conceptual model

Ted

A person in the real world, who just happens to be playing the role of the customer at this

point in 4me

We need a legal name for this person: A name registered with the government body within whose legal jurisdic4on we are

engaging with this person

An address is not just a couple of lines. Is it a residence? Does the customer have “in the town / in the country” homes? What sort of neighborhood is the house in?

The state that issued the license tells us the primary state of domicile of the person. The date of first issue tells us how long the person has been in the state

Data in & about a record also gives us technical context: which table in which database on

which server. When was this record created & by whom

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Modern Enterprise IT Environment: Ecosystem of meta driven systems

Smart Devices Modern Modular UI’s

Business Process Management

Customer Rela4onship Management

Service Oriented Architecture

Business Rules Engine

Accoun4ng Pla_orm

Master & Reference Data Management

Message and Data Integra4on

Data marts and Warehouse

Analy4cs Ecosystems

Enterprise Resource Planning

Enterprise Content Management

Security & En4tlement

Local & Wide Area Networks

On demand Data Centers & Virtual

Desktops

Modern development pla_orms are also heavily meta data driven: JEE, Spring, .Net, Cococa & iOS, Android

One primary role of designers and developers today is as translators between the “dialects” that these systems speak

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Case specific Process

Evolu2on of Metadata Driven Systems

R2RML

Parameter Driven Applica2on

Logic Parameters

Custom Extensions

Orchestra4on Rules

Parameters

Logic Orchestra4on Rules

Custom Extensions

Context

Metadata Driven Applica2on

Parameters Rule Fragments

Seman4c Model

Process Model

Enterprise Context

Logic Fragments

Dependency Tree

Parameters Orchestrator

Context Rule Fragments Logic Fragments

Current Generation Next Generation

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Scenario Report

En2tlement

Swim Lane

Business Rule

Test Case

Involves

Realized as

Segmented into

In a Performed by

Belongs to

Requires

History analyzed through

Metrics Generates

Realized as

Requires / generates

Behavior driven by

Validated by

The business (problem) space has metadata too…

Data

Func2on

Use Case Process

Actor

Task

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John Smith is a customer of the bank with a large investment porQolio. He is a cau4ous investor with a preference for energy stocks and commodity futures. PB banker is James M

Real world facts

<a human >

“John Smith”

Has name

<a por_olio account>

Operates

<a banker> [James M]

Is advised by

<a security holding> includes

<a security>

For Security

<a company> [Venture Solar]

<a corporate ac4on>

ini4ates Is no4fied

of

Issued By

<a banker> [Frank K]

Is advised by

plans

Public Informa4on

Works with

“Energy”

Ac4vity Area

plans

<a country> (Germany)

Tax Jurisdic4on

<a company> Benthik

Petroleum

Will become subsidiary of

<a country> (Syria)

Operates in

Has posi4ons in Headquartered in

<a state> [California]

<LOB> [Private Bank]

Works in

Works in

<LOB> [Corporate Advisory

Services]

John Smith holds posi2ons in Venture Solar, an energy company, currently headquartered in California, USA

John Smith currently works in Germany and is subject to German tax laws

Venture Solar is corporate customer. Frank K from corporate advisory services is currently helping them put together a reverse merger with Benthik Petroleum

Benthik Petroleum is authorized to operate in Syria

<a planned corporate ac4on>

Non public Informa4on

Taking this one step further… express actual data as graphs as well

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Mobile Device

Process

“John Smith”

Has name

<a por_olio account>

Operates

Is advised by

includes

<a security>

For security

<a corporate ac4on>

ini4ates Is no4fied

of

Issued By

Is advised by

<a planned corporate ac4on>

plans Non public informa4on

Public Informa4on

Works with

“Energy”

Ac4vity Area

plans

<a country> (Germany)

Tax Jurisdic4on

Will become subsidiary of

<a country> (Syria)

Operates in

Has posi4ons in Headquartered in

<a state> [California]

<LOB> [Private Bank]

Works in

Works in

<LOB> [Corporate Advisory

Services]

Por_olio Review Advise ac4on on a posi4on

Task Part of

Personal Banker

Customer Service Team

Performed by

iPad 3

On device

Mobile Applica4on On app

Reference Pla_orm

Table

Db2 on Mainframe

Data Pla_orm

CIF Aqribute

Legal Name EU Prospect Policy

enforces

Table Column

Applies to

Prospect informa4on

cannot be held for more than 3

months

Customer Master

CIF

<a company> [Venture Solar]

<a banker> [Frank K]

<a banker> [James M]

<a human >

Customer Name

Advisor Desktop

<a security holding>

<a company> Benthik

Petroleum

If we put this all together …

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We now know…

Which processes update data in table “X”?

•  SELECT ?process WHERE { ?table rdf:type db:Table . ?app app:canUpdate ?table . ?process proc:hasTask ?task . ?task proc:performedOn ?app }

Which development teams query the customer master table?

•  SELECT ?team WHERE { ?app app:canUpdate <CustomerMaster> . ?app app:maintainedBy ?team }

Which func4ons are impacted by an outage of the DB2 mainframe?

SELECT ?process ?func4on WHERE { { ?process prod:supports ?func4on . ?process prod:runsOn prod:DB2_Mainframe } UNION ?{ process prod:supports ?func4on . ?func4on prod:dependsOn prod:DB2_Mainframe } }

All the possible provenance alterna4ves for customer phone numbers

Which systems are impacted by this new EU policy?

Who are the possible downstream consumers of the field I am capturing on this screen?

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And change the way we write apps...

Pick the right connec4on pool paqern based on table proper4es

Invoke the right valida4ons based on the region of the customer and region of the user capturing the customer’s data

Enforce addi4onal valida4ons because of the needs of downstream processes

Enforce addi4onal process steps because of the poor controls upstream

Look across customers’ por_olios and iden4fy synergies across customers and foster collabora4ons

Based on how oPen folks have to override an upstream automated decision, add criteria upstream at the decision point

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1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS

Overview of the Program GeWng There …

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Enterprise Metadata Model … an Ini2al Inventory

Processes

Actors

R2RML

Tasks Interac4ons

Transac2onal Systems

CRM Core Systems ERP/corporate

Opera2onal Meta Data

KPI’s SLA’s Taxonomy

Rules and Policies

Compliance Risk Regula4on

Process Orchestra2on

Front office Back office Ops

Roles

Systems

Dependencies

Inputs & outputs

Interfaces

Messages

Organiza2onal Context

Departments Products & services

Legal Structure

Charter

KPI’s

Dependencies

Inputs & outputs

Legal Jurisdic4on

Legal En4ty

Informa2on + IT Asset Inventory Func4onal Inventory

Structured Data

Unstructured Data

Use Cases

Reusable modules

Opera4onal

Analy4cal

Documents

Knowledge Systems

Real World Overlap

Geography Business domain knowledge

Opera4onal Regula4ons

Common sense knowledge News & events

Business Regula4ons

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Program Overview – Possible func2onal End State

My Task Con

text Reference Data

Customer Transac2onal Interac2on

Eternal Data Sources

R2RML

My seman4c m

odel

Policy Overla

y

Governance / control / oversight

My seman4c m

odel

Process C

ontext + Current state

Access Layer (m

essage / service / fi

le /

Subject a

rea specific on

tologies

Analy2cs

Discovery

External

Ontologies

Product Org details

Transac2onal Systems

Account Trades GL

Opera2onal Meta Data

Process Role Taxonomy

Rules and Policies

Compliance Risk Regula4on

Process Orchestra2on

Front office Back office Ops

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Build a context model

Approach Overview

Build a context model

Unified View of the Enterprise

Encode the process models

Applica2ons access the seman2c knowledge

Build a sta2c business ontology

Process Models

Organiza4on Structure

Services & Products

External context

Current state: Ver 1.0 Future state: Ver 2.0

Concepts

Taxonomy

Rela4onships

Inferences

Reveal data through the ontology + process context

BPM Opera4onal Reports

Business rules

Applica2ons access the seman2c knowledge

Internal Data Ontology

Encoded process Encoded rules

Encoded Architecture External Data

Actors & roles

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SPAR

QL qu

ery fron

t end

Seman2c Repository

Business Process Models

Database Schema Metadata

Service Signatures + Data Types

•  Build repository & import meta data •  Boqom up seman4c modeling •  Import Business Process Models •  Import Enterprise Architecture models •  Import Interface specifica4ons •  Wrap data sources with SPARQL

Enterprise Architecture

Tasks

Basic Ontologies for each Domain

Interrogate and discover business & IT knowledge across the en2re ecosystem

BPMN

UML

XMI

XMI

WSDL / XML Schema / XML / JSON

XSLT

DB En4ty Model

3rd Party tools

Map to

Ontology

Database Tables

Document Stores

JDBC

JDBC Document Metadata

Actual Data

Phase 1: Integra2on

R2RML

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Notes on Process Modeling

Driv

er Arrive at lot

Hand over keys

Leave for meetings /

appointments

Keys

Driv

er

Par

k P

lus

Take key

UC 1: Capture

registration

UC2: Print tags

Attach copy to

keys

Return copy to

customer

Park car in designated

location

Hang up keys in booth

Return copy to

customer

TagLocationTime Serial numberRegistration

TagLocationtime Serial number

Volume:Max: 10 per hourNormal: 1 / 2 per hour

SLA:Prints in < 10 seconds

ScreenRegistration

ScreenConformation of tags

Volume informa4on for the process shown in diagrams

Data tags shown in each element

Where SLA’s are cri4cal, show them

All interac4ons must show data between users and systems

Use cases are color coded and number labeled.

Actor for the use case

Atte

ndan

t

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Meta data’s meta data

Building Domain Models

Inventory of terms to be modeled

Process context: Actor

Task

Interac4on

Importance

Role

Interfaces

Targeted process

Driv

er Arrive at lot

Hand over keys

Leave for meetings /

appointments

Keys

Driv

er

Par

k P

lus

Take key

UC 1: Capture

registration

UC2: Print tags

Attach copy to

keys

Return copy to

customer

Park car in designated

location

Hang up keys in booth

Return copy to

customer

TagLocationTime Serial numberRegistration

TagLocationtime Serial number

Volume:Max: 10 per hourNormal: 1 / 2 per hour

SLA:Prints in < 10 seconds

ScreenRegistration

ScreenConformation of tags

Indian Ci4zen

Suresh

Indian Passport

No: Z12345

Foreign ci4zen authorized to work

in the US

Suresh

US Social security number

No: Z12345

Holds passport

Is identified by

Person

Suresh

US Address

460 Park Ave S, NYC 11016

Works at

Statement Request

_ x _

Acme Bank Account

No: 12345 Has statement request

Prime holder on

Send to

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Avoid deep ontology modeling

Deep, complete seman4c models are very difficult to manage in a project context, and do not add significant value in the integra4on phase

Lessons Learned – Phase 1

Avoid “shortcuts” – model the real world

Tradi4onal database designs use a variety of short cuts to make real world complexity manageable. Le}ng these propagate into the seman4c model results in an ontology that is specific to the project context, and therefore does not survive well into later phases

Avoid abstract classes

Without a context to anchor them on, discussions on abstract classes tend to go into free fall, and added liqle to know value

Evolve classes & taxonomy from defining characteris2cs

Explicitly declared taxonomies proved difficult to reverse engineer in later phases of the project

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Phase 2: Ontology based Inferencing

•  Enrich OWL with inference rules •  Add inferencing capability to repository •  Migrate business rules to RIF •  Encode IT paqerns into RDF + RIF

Tasks

SPAR

QL qu

ery fron

t end

Seman4c Repository


Database schema meta data

Service signatures + data types


Enriched ontologies

BPMN

UML

XMI

XMI


XSLT

DB En4ty model 3rd Party tools

Map to

ontology

Database Tables

R2RML

Document Stores

JDBC

JDBC Document meta data

Actual data

Ontology Ba

sed Inferencing


Code generators (Java using Jena API + SPARQL)

Meta data for code generators (RDF) + design paqerns (RDF + RIF)

Applica4on logic driven by seman4c models

Applica4

ons

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Add backward chaining rules engine Add rule cura4on to govern “learned” rules

Tasks

Phase 3: Computer Intelligible Models (just star2ng)

SPAR

QL qu

ery fron

t end

Seman4c Repository


Database schema meta data

Service signatures + data types


Enriched ontologies

BPMN

UML

XMI

XMI


XSLT

DB En4ty model 3rd Party tools

Map to

ontology

Database Tables

R2RML

Document Stores

JDBC

JDBC Document meta data

Actual data

Ontology Ba

sed Inferencing


Meta data for code generators + design

paqerns

Reason

ing

Event history

Business Rules Rule Cura4on Process

Code generators (Java using Jena API + SPARQL)

Applica4on logic driven by seman4c models

Applica4

ons

Public Ontolgies OWL

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If a rule is not easy to define, check the model first

•  Majority of cases where a “reasonable” rule was proving difficult to implement, the root cause was a poor model of the real world. Fixing the model made rule defini4on easier

Retain graph models through all the layers

•  When consuming seman4c models in Java or other non-‐seman4c languages, we learned to retain the graph models through all applica4on 4ers

•  Majority of developers do not use Java objects as pure “logical models” of the real world. Instead they use families of classes to enable implementa4on of logic and ensure maintainability

•  Retaining the seman4c model through all 4ers was the only way to retain the Ontology overlays and enable use of business logic in all 4ers

Gaps in governance of ontology, rule and data cura4on will kill projects

•  Errors in ontology and rule models are rela4vely painless to fix, they can require people to walk back mul4ple threads of thought, which can be painful

Lessons Learned – Phases 2 & 3

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Data graphs •  Quads, not tuples (nothing in the default graph) •  Reified statements where provenance is important

Ontology Encoding •  OWL 2.0 •  Tight version control •  All ontologies must be uploaded to the repository

Elements in the Solu2on

Repositories •  Single scalable repository for meta data •  En4tlement enforced at named graph level •  Federated front end only to merge meta data with actual data

Inference levels •  Inferred rela4onships •  Inferred taxonomy •  Seman4c Web Rule Language = declara4ve rules. Rules produce new facts based on exis4ng facts in the model •  Rule Interchange Format = forward & backward chaining + other business rules variants: given an outcome, can figure out the star4ng configura4on that would lead to this result

Interfaces •  Interac4ve SPARQL endpoint for power users •  Custom HTML 5.0 screens + canned queries + model naviga4on for occasional users •  Import + export through RDF / TTL / TRIG format. Convert to RDF / TTL / TRIG using custom Java code

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1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS

THANK YOU About MphasiS MphasiS an HP Company is a USD 1 billion global service provider, delivering technology-‐based solu4ons across industries, including Banking & Capital Markets, Insurance, Manufacturing, Communica4ons, Media & Entertainment, Healthcare & Life Sciences, Transporta4on & Logis4cs, Retail & Consumer Goods, Energy & U4li4es and Governments around the world. MphasiS’ integrated service offerings in Applica4ons, Infrastructure Services and Business Process Outsourcing help organiza4ons adapt to changing market condi4ons and derive maximum value from IT investments. For more informa4on about MphasiS, log on to www.mphasis.com

Presenta2on by Suresh Nair Vice President & Chief Architect, Banking & Cap Markets

[email protected]

Semantic Meta Data Driven Enterprise v2 - OMG | Object ... 1November2013|) Proprietary)and)conﬁden4al)informaon.)©)2013)MphasiS ) Modern*Enterprise*IT*Environment:* Ecosystemofmetadrivensystems

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Semantic Meta Data Driven Enterprise v2 - OMG | Object ... 1November2013|) Proprietary)and)conﬁden4al)informaon.)©)2013)MphasiS ) ModernEnterpriseITEnvironment: Ecosystemofmetadrivensystems