Kamanja: Driving Business Value through Real-Time Decisioning Solutions

© 2015 ligaDATA, Inc. All Rights Reserved.
Driving Business
Value Through  
Real-Time
Decisioning
Solutions
July 2015
Download, Forums, Docs, Events http://Kamanja.org
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Outline
Motivation Case Study – Modeling Department
Review Mining and Big Data Tools
Solution: Predictive Markup Modeling Language (PMML)
Reviewing Big Data Space and Real Time
Kamanja Integration (Open Source PMML)
Use Cases, Demo, Architecture

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Audience Survey (show of hands)
Data Mining Experience
__% Read or heard about
__% Class or competition
__% Put a model into production
__% Have put 10+ models in production
__% Have put 75+ models in production
Big Data Experience
__% Read or heard about
__% Class or exploration project
__% Put a system into production
__% System with 3+ OSS in prod
__% System with 6+ OSS or PB+ in
production
Extensive Data Mining AND Big Data Experience
__% with 10+ models AND 3+ OSS
__% with 75+ models AND 6+ OSS / PB+
Overlap on extensive experience is rare
This is what Kamanja helps with

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Case Study of a Modeling Department
Financial Fraud Detection
CONTEXT
•  3 modelers, 2 data infrastructure people in department
•  Over 3 dozen predictive models in production, high $$$$ and visibility
•  Separate Operations group deploying models
PROBLEM
•  Models were getting stale
•  “Spinning Plates” between short term solutions
•  2 months for a full model training investigation
•  2 months to put a model into production (OUCH)
Had to completely re-code the preprocessing and model scoring
Operations had One process to deploy a regression
Operations had a different process to deploy a decision tree

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INDUSTRY REVIEW to answer:
•  How common is it to use many algorithms or tools in a project?
•  What is an easier way to deploy models?

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http://www.kdnuggets.com/polls/2015/analytics-data-mining-data-science-software-used.html
In the industry, many algorithms and
tools are used
Need to simplify DEPLOYMENT

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http://www.kdnuggets.com/2015/06/data-mining-data-science-tools-associations.html
Independent use of tools

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http://www.kdnuggets.com/2015/06/data-mining-data-science-tools-associations.html
Tools used in combination

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Scoring
Engine
(Kamanja)
PMML Diagram
Predictive Modeling Markup Language
Training & test data
(batch)
Data
Mining
Tool File, Save As
PMML
PMML
File
PMML
Producer
PMML
FileScoring data
(real time streaming)
Output data has
new score field
Training Project Phase
Production Scoring Project Phase
Full model
specification
PMML Consumer

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Given industry fragmentation,
PMML is a solution
PMML Producers (18 companies)
•  R (Rattle, PMML)*
•  RapidMiner
•  KNIME*
PMML Consumers (12 co)
•  Zementis
•  IBM SPSS
•  KNIME
•  Microstrategy
•  SAS
•  Kamanja* (Open Source)
•  Spark (MLib)* * = Open Source
•  Weka*
•  SAS Enterprise Miner
PREDICTIVE
Naïve Bayes
Neural Net
Regression
Rules
Scorecard
Sequence
SVM
Time Series
Trees
DESCRIPTIVE / OTH
Association Rules
Cluster, K-Nearest Nb
Text Models
model ensembles &
composition
(i.e. Gradient
Boosting)

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SOLUTION OBJECTIVES
1) Support a wider variety of algorithms and software (increase accuracy)
2) Decrease time on putting models into production (incr analysis time)

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SOLUTION OBJECTIVES
1) Support a wider variety of algorithms and software (increase accuracy)
2) Decrease time on putting models into production (incr analysis time)
SOLUTION
1) Train models in SAS Enterprise Miner, R (PMML Producers)
2) Score models with a RESTful call to a PMML Consumer (Zementis)
Predictive Modeling Markup Language (PMML) is a type of XML
RESULT
1) By supporting more software & algorithms – MORE ACCURATE!
2) PUT MODELS INTO PRODUCTION from 8 weeks to down to 2-5 days!
Greatly increased throughput of training new models!

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Outline
Motivation Case Study – Modeling Department
Review Mining and Big Data Tools
Solution: Predictive Markup Modeling Language (PMML)
Other Uses of Real Time Decisioning
Reviewing Big Data Space and Real Time
Kamanja Integration (Open Source PMML)
Use Cases, Demo, Architecture

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Other Uses of PMML or
Real-Time Decisioning
Complex Event Processing (CEP)
•  Possibly 100’s of concurrent data streams
•  Apply rule logic, select, aggregate
•  Select action on elements in stream
Enterprise Applications, During …
•  customer call or chat: recommendations to improve service
•  card transaction: offer credit increase
•  web application: pre-approval
•  web transaction: recommend other product(s)
•  MOOC: customize training speed for the student
(Custom Java Model)

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Real Time Computing
OSS Technology Stack
Integration with Kamanja
Kamanja
(PMML/Java/Scala Consumer)
High level languages / abstractions
Compute
Fabric
Cloud, EC2
Internal Cloud
Security
Kerberos
Real Time
Streaming
Kafka,
MQ
Spark*
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Data Store
HBase,
Cassandra,
InfluxDB
HDFS
(Create
adaptors to
integrate
others)
Resource
Management
Zookeeper,
Yarn*,
Mesos*
High Level Languages /
Abstractions
MLlib* (PMML Producers)

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Real Time Open Source Systems (OSS)
Kamanja and Spark are good Compliments
Clarify with a feature list,
Use case to work together

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Higher Requirements in  
Financial Services or Health Care
Compared to social media or web apps
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Legal Compliance to meet exacting
technical standards
•  Losing (or duplicating) a bank transaction
•  Losing a medical record
•  Executives or employees can GO TO JAIL
What is different about these industries?
•  Regulatory requirements requires 100% data protection
•  Security
•  Auditability
•  Lineage
•  ZERO data loss
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•  Migrated from mass generalized
communications to real time
personalized alerts
•  Increased messaging effectiveness
of 400% lift in conversion to digital
•  Full integration with Mainframe
•  Leverage streaming transaction
and customer account data
Business Objectives
•  Reduce operating cost of Calling Centers
•  Increase customer adoption of digital channels
•  Interact with Customer at point of transaction
IT Objectives
•  Implement cost effective and scalable platform
•  Satisfy financial services security and  
compliance req.’s
•  Integrate with existing core systems
Driving Digital Adoption:
the Bank’s Call Center
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Results
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Medical Company use
of Kamanja
Lines of Business
Run Medco models
supplying client's
intelligence based upon
model ﬁndings  
(using multi tenant deployment when
appropriate)
Run Customer models  
on Medco hardware 
(on Medco owned  
customer private net)
Consult/partner with
Medco customers
providing software
solutions to be run on
Customer net

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•  Clinicians (knowledge experts) develop heuristic based rule set models
•  The initial model was COPD (Chronic Obstructive Pulmonary Disease) risk
assessment
•  Models are expressed with a Domain Specific Language (DSL) they developed
•  DSL models are transformed to PMML for Kamanja
•  Models consume current + prior related messages over “look back period”
Save the “assertions” of a patient in the database (beyond standard PMML)
•  Medco plans to integrate the DSL with their ontology data modeling effort
•  Goal is to generate new models as their “medical world” ontology evolves
Medical Company use
of Kamanja

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DECISION
GATEWAYS
Representative Kamanja  
Solution Architecture
MAINFRAME
DB2
SERVER
INTEGRATION
CDC
Kafka
Inbound
Queue
Kamanja
Security Management
Error Management
Metadata Service/Cache
Storage Service/Cache
Message
Construction
Decision  
Engine
Output  
Handler
Transform
Compute
MakeObj
Parallel
DAG
Executor
DAG
Optimizer
DAG
Generator
Change
Listner
Output
Generator
Output
Disctributor
DBs
Apps
Notiﬁcation
Engine
DATA SOURCES
DW
Customer
Preferences
Kafka
Outbound
Queue
HBase - History
HDFS – Long term storage
Zookeeper – Resource Management
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Performance 
Characteristics
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Performance
•  Throughput of million messages/second
•  Uses commodity hardware
Scalability
•  Linear scalability vertically and horizontally
•  Data partitioning support
•  Runtime multi-model optimizations to
supports thousands of models
•  Consistent performance on hundreds of
models and thousands of rules
Built for IoT
data volumes
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Data
Transformer
Data History
(Cassandra,
HBase)
Metadata
(expanded in next slides)
Model
Runtime
Output
Dispatcher
Kafka
Queue
Input
Adapter
Output
Adapter
Next
Process
Kamanja Engine
Kamanja Execution
Flow on a Node
Storage
Adapter
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Metadata
Functions
(in PMML, Scala as
User Defined Func (UDF))
Models
(PMML Rule Set, i.e.
fraud, attrition)
Messages
(from input queue, real
time records)
Containers
(i.e. a record or lookup table
to provide context, priors)
Types
(i.e. array of patients, Dr’s,
types of containers)
Concepts
(PMML created fields,
preprocessing, scores)
Metadata API
Elements
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Metadata
Metadata API
Scoring Engine
Manager
(within a model)
Model Manager
(activate, control
a DAG of many
models)
PMML Producer, or
application
Admin App, used by DevOps
Activate PMML Model or DAG
Rest API
Metadata API
Subsystems
Configuration
(Cluster, Engine, Model Compilation)
Kamanja Engine
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Model Runtime
Kamanja Runtime
Model Execution
Transformer
Data History / Metadata,
(HBase, Cassandra, ..)
Msg
Storage
Adapter(s)
Metadata
Instance
Model
Object
Model
Factory
1) Message rec by
runtime engine
2) Metadata is checked
To see what model is
Interested in the message 3) Model object
Is instantiated
5) Msgs committed
to history
4) Model is executed
on the Message obj
6) Output of the model
is returned to the engine
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If the node that crashes is a Kamanja Slave node
•  The Kamanja Leader Node rebalances over all Kamanja nodes
•  Each message is processed EXACTLY ONCE
•  A Bank needs to process a transaction ONCE AND ONLY ONCE
•  Look at the state of every message through each step
If the Kamanja Leader node goes down,
•  The next node on the list becomes the Leader, then rebalance
COMPARE TO:
•  Spark and Storm would execute each message AT LEAST ONCE (but may
process a message 2, 3 or 4 times…).
•  The expectation is for the application to handle possible dup.
What happens when
a node goes down?
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Kamanja Integration
Points
•  We provide with an enterprise friendly license
(No GPL License virus to infect the entire system)
•  Adaptors: for any data flow
Kafka, IBMs MQ, Hbase, Cassandra, InfluxDB, Zookeeper, Spark
•  User Defined Functions:
Provide a JAR file or Scala function
•  Custom Java Model
Can skip PMML, leverage Adaptors and UDFs
Import generated Java code

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Deploy Predictive Models and
Rules in 1/100th the time it takes
today
•  Kamanja is an open source, real time decisioning
engine
•  Hardened to meet strictest requirements of Financial
Services, Healthcare and scalable to handle IoT
•  Kamanja Enables Developers and Data Scientists to
reduce time to deploy Rules and Predictive Models
•  Kamanja integrates with your Big Data ecosystem

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Planned Kamanja
Differentiation
•  Model management, enable DevOps for models

DevOps: automated testing, validation, deployment and rollback

A/B testing to competitively roll out model update, scheduling
•  Enterprise Level Security and Multiple-Tenancy

Integration using Kerberos

Role based security for model management

Security at field level for models, “need to know/access”
•  Multi tenancy

partition internal groups in different tenancies

Data isolation, resource management, SLA support
•  Data Integration

Built-in integrations for social data and third party data

Can consume 100s of different event and document types

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Planned Kamanja
Diﬀerentiation
•  Performance and Scale

Dynamic scaling – enlarge and shrink as needed, based on load

Leap in performance by generating native code (vs. Java)

Cost aware execution in cloud environment
•  Extensive integrations with enterprise queue, storage and indexing

MQ, HBase, Cassandra, RDBMS, Elastic Search, Zookeeper
•  Domain speciﬁc libraries and model templates to speed up preprocessing,
business logic and algorithms

Try out 
Kamanja
CONFIDENTIAL
Download, Forums, Docs, Events http://Kamanja.org
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Kamanja: Driving Business Value through Real-Time Decisioning Solutions

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