Unlocking the Power of Seman4c Knowledge Mastering Enterprise Metadata with Seman2c Modeling 1 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Seman2c Model: A data model linked to the real world through a conceptual model Ted
Unlocking the Power of Seman4c Knowledge Cust. ID 2 Name A person in the real world, who just happens to be playing the role of the customer at this point in 4me Address 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 Drivers License SID: ORCL 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? 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 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 The seman4c model is not just part of the metadata, it is the metadata 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Unlocking the Power of Seman4c Knowledge Modern Enterprise IT Environment: Ecosystem of meta driven systems 3 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Evolu2on of Metadata Driven Systems Next Generation
Current Generation
Metadata Driven Applica2on Context Logic Parameters Orchestra4on Rules Orchestra4on Rules Dependency Tree Rule Fragments Logic Fragments Context Parameters Custom Extensions Enterprise Context Seman4c Model Case specific Process 4 Custom Extensions Parameters Process Model Orchestrator Unlocking the Power of Seman4c Knowledge Parameter Driven Applica2on R2RML Logic Rule Fragments Logic Fragments Parameters 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS The business (problem) space has metadata too… Unlocking the Power of Seman4c Knowledge Scenario 5 Report History analyzed through Involves Use Case Process Realized as Segmented into Metrics Generates Task In a Requires / generates Realized as Func2on Data Behavior driven by Business Rule Performed by Validated by Swim Lane Belongs to Actor Test Case Requires En2tlement 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Taking this one step further… express actual data as graphs as well Unlocking the Power of Seman4c Knowledge Real world facts 6 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 John Smith holds posi2ons in Venture Solar, an energy company, currently headquartered in California, USA “John Smith” Has name <a country> (Germany) 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 por_olio account> Tax Jurisdic4on Is advised by Operates <a human > Has posi4ons in Public Informa4on <a banker> [James M] Works in <LOB> [Private Bank] Is no4fied of Works with <a corporate ac4on> ini4ates <a banker> [Frank K] <a state> [California] plans plans Works in Non public Informa4on <LOB> <a planned [Corporate Advisory corporate Services] ac4on> <a security holding> For Security Headquartered in <a company> [Venture Solar] Will become subsidiary of Is advised by includes Issued By <a security> Ac4vity Area <a company> Benthik Petroleum “Energy” Operates in <a country> (Syria) 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS If we put this all together … Process Por_olio Review Task Part of Mobile Device Unlocking the Power of Seman4c Knowledge iPad 3 7 Advise ac4on on Personal Banker a posi4on Performed by On On Mobile Applica4on device app Advisor Desktop EU Prospect Policy Prospect informa4on cannot be held for more than 3 months Customer Service Team Data Pla_orm Table Column Db2 on Mainframe Customer Name Table CIF Aqribute CIF Legal Name Reference Pla_orm Customer Master enforces “John Smith” Applies to <a por_olio account> Has name <a country> (Germany) Tax Jurisdic4on Operates <a human > Public Informa4on Works in <LOB> [Private Bank] Is no4fied of Works with <a state> [California] Has posi4ons in Headquartered in Is advised by <a banker> [James M] includes <a corporate ac4on> ini4ates <a company> [Venture Solar] Will become subsidiary of Is advised by <a banker> [Frank K] plans plans Works in Non public informa4on <LOB> <a planned [Corporate Advisory corporate Services] ac4on> <a security holding> For security Issued By <a security> Ac4vity Area <a company> Benthik Petroleum “Energy” Operates in <a country> (Syria) 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS We now know… Which processes update data in table “X”? Unlocking the Power of Seman4c Knowledge •  SELECT ?process WHERE { ?table rdf:type db:Table . ?app app:canUpdate ?table . ?process proc:hasTask ?task . ?
task proc:performedOn ?app } 8 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? 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS And change the way we write apps... Unlocking the Power of Seman4c Knowledge Pick the right connec4on pool paqern based on table proper4es 9 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS GeWng There … Unlocking the Power of Seman4c Knowledge Overview of the Program 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Enterprise Metadata Model … an Ini2al Inventory Processes Unlocking the Power of Seman4c Knowledge Actors 11 Tasks Interac4ons Roles Dependencies Interfaces Systems Inputs & outputs Messages Informa2on + IT Asset Inventory Func4onal Structured Unstructured Inventory Data Data Use Cases Opera4onal Documents Reusable modules Analy4cal Knowledge Systems Organiza2onal Context Products & Legal Departments services Structure Charter Dependencies Legal Jurisdic4on KPI’s Inputs & outputs Legal En4ty Transac2onal Systems CRM Core Systems ERP/corporate Process Orchestra2on Front office Back office Ops Rules and Policies R2RML Compliance Risk Regula4on Opera2onal Meta Data KPI’s SLA’s Taxonomy Real World Overlap Business domain knowledge Geography Business Regula4ons Common sense knowledge News & events Opera4onal Regula4ons 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS 12 Subject area specific ontologies Process Context + Current state Customer Product Org details Transac2onal Systems Account Trades GL Process Orchestra2on Front office Back office Ops Opera2onal Meta Data Process Role Taxonomy Rules and Policies R2RML Compliance External Ontologies Discovery Reference Data Access Layer (message / service / file / Analy2cs My seman4c model My seman4c model Governance / control / oversight Policy Overlay Unlocking the Power of Seman4c Knowledge Transac2onal Interac2on My Task Context Program Overview – Possible func2onal End State Risk Regula4on Eternal Data Sources 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Approach Overview Build a context model Build a context model Process Organiza4on Models Structure Unlocking the Power of Seman4c Knowledge Services & Products 13 Build a sta2c business ontology External context Current state: Ver 1.0
Future state: Ver 2.0
Concepts Rela4onships Taxonomy Inferences Encode the process models BPM Opera4onal Reports Business rules Actors & roles Applica2ons access the seman2c knowledge Applica2ons access the seman2c knowledge Reveal data through the ontology + process context Unified View of the Enterprise Internal Data Ontology Encoded process Encoded rules Encoded Architecture External Data 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Phase 1: Integra2on Seman2c Repository 14 Database Schema Metadata Service Signatures + Data Types Enterprise Architecture XMI Map to Ontology Interrogate and discover business & IT knowledge across the en2re ecosystem SPARQL query front end Unlocking the Power of Seman4c Knowledge Business Process Models BPMN 3rd Party tools WSDL / XML Schema / XML / JSON XSLT XMI UML Basic Ontologies for each Domain JDBC Actual Data Tasks •
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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 DB En4ty Model R2RML JDBC Document Metadata Database Tables Document Stores 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS 15 Arrive at
lot
Leave for
meetings /
appointments
Tag
Location
time
Serial number
Keys
Take key
Use cases are color coded and number labeled. Screen
Registration
Park Plus
Actor for the use case Return
copy to
customer
Hand
over keys
Volume:
Max: 10 per hour
Normal: 1 / 2 per hour
Volume informa4on for the process shown in diagrams Driver
Attendant
Unlocking the Power of Seman4c Knowledge Driver
Notes on Process Modeling UC 1:
Capture
registration
Return
copy to
customer
Attach
copy to
keys
All interac4ons must show data between users and systems Park car in
designated
location
Hang up
keys in
booth
Screen
Conformation
of tags
UC2: Print
tags
SLA:
Prints in < 10 seconds
Where SLA’s are cri4cal, show them Tag
Location
Time
Serial number
Registration
Data tags shown in each element 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Building Domain Models Driver
Targeted process Arrive at
lot
Return
copy to
customer
Hand
over keys
Indian Ci4zen Suresh Leave for
meetings /
appointments
Volume:
Max: 10 per hour
Normal: 1 / 2 per hour
Return
copy to
customer
Driver
Screen
Registration
Park Plus
Unlocking the Power of Seman4c Knowledge Take key
16 Indian Passport No: Z12345 Tag
Location
time
Serial number
Keys
UC 1:
Capture
registration
Holds
passport
Attach
copy to
keys
Park car in
designated
location
Hang up
keys in
booth
Foreign ci4zen authorized to work in the US Screen
Conformation
of tags
UC2: Print
tags
SLA:
Prints in < 10 seconds
Tag
Location
Time
Serial number
Registration
Is
identified
by
Suresh Inventory of terms to be modeled Meta data’s meta data Person Suresh Process context: Actor Role Task Importance Interac4on Interfaces US Social security number US Address Works at
Prime
holder on
460 Park Ave S, NYC 11016 Send to
Statement Request Acme Bank Account No: 12345 No: Z12345 Has
statement
request
_ x _ 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Lessons Learned – Phase 1 Avoid deep ontology modeling Unlocking the Power of Seman4c Knowledge Deep, complete seman4c models are very difficult to manage in a project context, and do not add significant value in the integra4on phase 17 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Phase 2: Ontology based Inferencing 18 Code generators (Java using Jena API + SPARQL) Database schema meta data Service signatures + data types XMI Map to ontology Business Process Models Ontology Based Inferencing Applica4on logic driven by seman4c models Seman4c Repository SPARQL query front end Applica4ons Unlocking the Power of Seman4c Knowledge Interrogate and discover business & IT knowledge across the en4re ecosystem Enterprise Architecture BPMN 3rd Party tools WSDL / XML Schema / XML / JSON XSLT XMI UML Enriched ontologies Meta data for code generators (RDF) + design paqerns (RDF + RIF) JDBC Tasks •  Enrich OWL with inference rules •  Add inferencing capability to repository •  Migrate business rules to RIF •  Encode IT paqerns into RDF + RIF DB En4ty model R2RML Actual data JDBC Document meta data Database Tables Document Stores 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Phase 3: Computer Intelligible Models (just star2ng) 19 Tasks Add backward chaining rules engine Add rule cura4on to govern “learned” rules Map to ontology XMI Database schema meta data Service signatures + data types Enterprise Architecture Enriched ontologies Business Rules Meta data for code generators + design paqerns Event history R2RML BPMN 3rd Party tools DB En4ty model WSDL / XML Schema / XML / JSON XSLT XMI Reasoning Code generators (Java using Jena API + SPARQL) Business Process Models Ontology Based Inferencing Applica4on logic driven by seman4c models Seman4c Repository SPARQL query front end Applica4ons Unlocking the Power of Seman4c Knowledge Interrogate and discover business & IT knowledge across the en4re ecosystem UML Rule Cura4on Process OWL JDBC Actual data JDBC Document meta data Public Ontolgies Database Tables Document Stores 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Lessons Learned – Phases 2 & 3 If a rule is not easy to define, check the model first Unlocking the Power of Seman4c Knowledge •  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 20 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Elements in the Solu2on Data graphs •  Quads, not tuples (nothing in the default graph) •  Reified statements where provenance is important Unlocking the Power of Seman4c Knowledge Ontology Encoding 21 •  OWL 2.0 •  Tight version control •  All ontologies must be uploaded to the repository 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 •
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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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS Unlocking the Power of Seman4c Knowledge THANK YOU Presenta2on by Suresh Nair Vice President & Chief Architect, Banking & Cap Markets suresh.nair@mphasis.com 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 1 November 2013 | Proprietary and confiden4al informa4on. © 2013 MphasiS