© 2013 WGSigma Software, Inc. | Confidential

William Guinn

SVP and CTO

San Jose, CA

william.guinn@wgsigmasystems.com

WGSigma Systems

Architecting intelligent real-time systems processing

billions of events a day

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© 2013 WGSigma Software, Inc. | Confidential

Hundreds or thousands of systems

performing transactions in silos with no

collective intelligence

The Universal Corporate Challenge:

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BILLING

CORE

PP / RT

TABLES

UPS

Unified

Provisioning

PPOL / RTOL

Price Plan Online

Reference tables

Configuration

CSM Online

Customer Mgmt.User interface

MAF

Mssg Acq

& Format

CH

Customer Hierarchy Mgmt

Discount Engine

BAN Thresholds

& Aggregation

EMS

Error Mgmt System

CDR errors

Corp eCare

Account Management,

Bill View & Payment

LMS

Letter Management

Collection/Notices

ADD

Invoice s

A/R

Accts Receivable

General Ledger

Collections

Risk/Path/Agency

CSM Server

Customer Mgmt Syst.

Primary Business Logic

HP IUM

PPD Middleware

LMPS/NM

Mediation

AG

Activation G/W

WAC

Wireless Activation Ctr

fastACT

Activations Lounge

WOF

Web Order Form

OBOT

One Bill Orders

HUB

Vitria

Conso

One Bill

SAP

Revenue

Reporting

EDW

Business Intelligence

Reports/GARS

WebFocus

BCV

DB Copy

Left Turn Mgr

Order Completion

Cons. Portal Business Portal

Corporate Access

Network

Prepaid (ALU)

CDMA/HSPA

OCS(Openet)

LDAP

LTE

AAA

DEALERS

RETAIL

CSR/ASR WORLD / CFR Viewer

Report Listing

CALL

CENTERS

CSRs

CUSTOMER ACCESS Bills

TrueComp (Callidus)

Commissioning

Scorecard

Dashboard

Extracts

Informatica

Recon Tool

Reconciliation

MAX

Pagers

Single View

CRM

IVR

Call routing

EPC

Enterprise Product Catalog

IDR

Online Bill Manager

CMT

Campaigns

MoneyMap

cVidya RA

SS

Self-Service Back-End

EDIRIS

Payments

Printshop

OneRT

Recom.Offers

ECT

Email Capture

HEAT

Trouble Ticket

BM Portal

Forms

One Source

Content Mngmt

MCO

Mass Changes

IRIS

Equifax

Credit Eval.

IP APPs

PPD PAD

Emergis

CC Payment

RMS

Equipment Mgmt

OEM/ SIMS

Inventory

SAS

BI Tool

API - Link

T

o A

PI

- L

ink

E-Glue

OPS

ARIBA

Empl.Payments

CLEARING

Roaming

MPS

Rating

About 20% of the Customer Systems in a large Telco

Mosaix

Dialer

SMG/VMC

WNP

PLM

Notifications

Corp.sec.

Hot List DB

NM1

Database

DNC

Do not call

FraudX

Fraud

CSM

Interfaces

Billing

Bill Cycles

NM

Number Mgmt

CMS

© 2013 WGSigma Software, Inc. | Confidential 4

Create a collective knowledge of the

operation where processing decisions are

holistically optimized

….Patients

….Customers

…Subscribers

….Account Holders

Real Time Intelligence for Large Scale Environments

The Goal:

03.15.10 © 2013 WGSigma Software, Inc. | Confidential

Closed Loop Event – Condition – Action

Events Decision Engine

Container Container

Actions

Application Server – Elastic Data Grid Based

“Sesame” Performance cache

RDF

Triple Store DB

Event

Ingestion

Scheduled

Events

Semantic

Inference

Engine

and

Business

Rules Bayesian

Belief

Network

Events

Operational Systems Any operational system

Data Sources Any internal or external system

Structured or Unstructured

Event Collector

X Z X

Integration

Framework

Y

Network Business Intelligence

Other

System Other

System Other

Systems

Other

System Other

Systems

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03.15.10 © 2013 WGSigma Software, Inc. | Confidential

Inferencing

Inference

Engine

and

Business

Rules Bayesian

Belief

Network

Semantic Concept Model

Defines concepts and meaning

Defines relationships between

concepts

Semantic model capabilities

reasoning

The model contains the relationship between

concepts including the dependencies

The model defines the concepts including the high

level business concepts

Semantic Concept Model Machine learning trained models

BBN probabilistic model

Recommendation model

Other Machine learning

algorithms

When a concept is dependent on “machine learned”

information the inference engine manages the invocation and

timing of interfacing

Machine learning

When an event occurs the event handler rule fires for that event

Evaluates the event message

Evaluates the existing ontology

Determines which semantic instances to create or update

When any data changes, the inference engine fires in a “When -

Then” style of computing, updating all “Automatic” concepts.

Custom concept rules are fired if necessary. This creates a chain

of updates

When a “on demand” concept is needed the inference engine

finds and computes all of the dependant concepts

Inference

Event Create semantic concepts from events

Concept Define a custom business

concept A set of custom rules actioning business

policy

Action

Business policy Rules

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03.15.10 © 2013 WGSigma Software, Inc. | Confidential

Use Case: Customer Interaction Prediction and Best Action

Chronology of events

Interactions

Bills

Orders

Payments

Collections

Charge dispute

Individual

Customer

Pay instructions

Device Activated

Device heartbeat

Subscriptions

Device changes

Transformed into a connected

graph of business concepts

Subjective "good payer"

Patterns "always pays 2 days late"

Trends “improving payer"

Geospatial “within 5 miles of the tower"

Time “within 5 minutes of an outage"

Probability “probably will call about the bill"

Absence of occurrence “missed payment"

Relationship between " friend of a friend"

Events collected in

real time Plan Overage

Bill greater than last month

Prorates

Roaming charges

Third Party Charges

Abnormal fee

Rate increases

Charge dispute

First bill

Past due amount

Pay bill

Customer Cancellation

Reactivation

Device activation

Device education

Device exchange

Device Lost

Device not working

Device resume

Service data not working

Service text not working

Service voice not working

Subscription cancellation

Wrong plan

Predictions & Actions

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03.15.10 © 2013 WGSigma Software, Inc. | Confidential

• Use in memory elastic data grid

• New hardware nodes can be added and removed dynamically

• Data is replicated to one other node (no single point of failure)

• If a server fails, the backup becomes the primary seamlessly

• Partition grid across objects

Scalability & Resilience

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• Graphs are partitioned across multiple triple store instances

• A single index instance defines the location of each graph

• Historical data is subdivided into graphs by timeframe (e.g. quarters)

03.15.10 © 2013 WGSigma Software, Inc. | Confidential

Make it Business Friendly

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Search Map RELATIONSHIP MAP DEPENDENCY MAP TAG PATH Filter Map Library

EVENTS ENTITIES ACTIONS PROCEDURES

Customer

Acquisition Date

Status

Status History

Customer Accounts

Customer Bill

Customer Devices

Customer Action

Entity Name

Composer Demo Application

CONSTANTS +

BUSINESS ENTITIES & CONCEPTS +

Type

Call Frequency

Customer

Customer Accounts

Customer Bill

Customer Problems

Interactions

Subscriptions

Acquisition Date

Status

Status History

Type

Call Frequency

Churn Propensity

Collection Risk

Payment Pattern

+

Customer -

+

- Churn Propensity

Collection Risk

Payment Pattern

Customer Payment Pattern

Customer Payment Pattern

EDIT DEPLOY VIEW

LAST MODIFIED: 02/12/2010 MODIFIED BY: Greg Sloan VERSION: 1.3

DESCRIPTION: Determine the payment pattern for the customer by evaluating payments determining good payer, bad payer, worsening payer or improving payer

2. If all bills have Payment Timeliness

then is good payer Payment Pattern

COMPUTED:Automatically

Find all of the 1. Previous Bills Within 6 months

equal to early

3. If all bills have Payment Timeliness

then is bad payer Payment Pattern

equal to late otherwise

4. If at least 75% Earlier Bills

late

of otherwise are early or on time

and later bills are then is worsening payer Payment Pattern

5. If At least 50% Earlier Bills

On time or early

of Late

and all later bills are then is Improving payer Payment Pattern

are

Customer

Description. The role played by an Individual or Organization in a business relationship with the

service provider in which they intend to buy, buy, or receive products or services from the service p

Customer Problems

03.15.10 © 2013 WGSigma Software, Inc. | Confidential

Technology Stack

Technology Purpose

TopBraid Composer Ontology Modeling

Java Event Pipeline, Decision Engine, Integrations

R Statistical and Predictive Modeling

ETL Event ingestion - Extract, Transform, Mapping Relational to Graph

Red Hat

BPMS & Drools

Forward and backward chaining rules, process definition and

control

Red Hat

HornetQ

JMS Queue persistent multi-threaded input and output (publish-

subscribe)

Gigaspaces Data Grid

Apache UIMA + Solr Natural Language Processing, Semantic Searches, Content

Analytics

Norsys Netica BBN

Franz Allegrograph Triple Store (RDFS)

Apache Cassandra Time series data store 10