The Briefing Room with Gregory Piatetsky and Sybase, an SAP Company Slides from the Live Webcast on Apr. 17, 2012 Predicting the future happens all the time these days, in all kinds of business situations. And now, there are more options than ever for leveraging the power of predictive analytics. That's partly due to the rise of open-source projects like Hadoop and partly because of the maturation of traditional analytical platforms. The bottom line is that predictive power is now at the fingertips of a much broader audience. Register for this episode of The Briefing Room to hear KDnuggets purveyor Gregory Piatetsky explain why data mining and predictive analytics are experiencing a renaissance. He'll be briefed by Joydeep Das of Sybase, an SAP Company, who will outline how Sybase IQ facilitates three approaches: the traditional "pull" model used by SAS, SPSS and other analytic workbenches; a newer "push" model where much of the heavy lifting takes place in the database; and a very novel hybrid "push-pull" model that leverages Hadoop.

1. PREDICTIVE ANALYTICS WITH SAP SYBASE IQ
JOYDEEP DAS
PRODUCT MANAGEMENT
APRIL 17, 2012
CON
FIDE
N TIAL

2. BUZZ IN THE INDUSTRY - I
Unleashes Business Value
New Strategies &
Business Models
Business
Value*
Operational Revenue
Efficiencies Growth
© 2012 SAP AG. All rights reserved. 2

3. BUZZ IN THE INDUSTRY - II
New Demands, New Opportunities
Mining New Sources of Data
• Large volumes of data beyond structured
• Text, social, clickstream, geospatial
• Interactive, on-the-fly analysis
• New methodologies
• e.g. social network analysis
Proliferation
• Increased usage by non-technical
business users
• Embedded in applications
• e.g. recommendation engines in
CRM
• Platform has to be accessible and support
more users!
© 2012 SAP AG. All rights reserved. 3

4. SAP Sybase IQ
A Powerful Platform For Predictive Analytics – Sample Case Study #1
Sybase
IQ
significantly
enhanced
AOK
Hessen’s
ability
to
handle
complex
business
predic0ons
involving
mul0-­‐dimensional
analysis
of
many
input
variables.
AOK
Hessen,
a
large
health
insurance
company,
searches
for
pa+erns
across
all
exis8ng
informa8on
–
medical
treatment,
prescrip8on,
insurance
benefits
–
simultaneously.
“The
divisions
currently
using
the
tool
run
a
significantly
greater
number
of
analyses
than
ever
before.
They
keep
discovering
new
ways
of
drilling
down
into
data
while
working
with
the
soCware.”
-­‐
Michael
Shimmelpfennig,
Service
Manager,
IT-­‐Business
Department
© 2012 SAP AG. All rights reserved. 4

5. SAP Sybase IQ
A Powerful Platform For Predictive Analytics – Sample Case Study #2
Sybase
IQ
enables
Playphone
to
gain
significant
compe00ve
advantage
through
new
capabili>es
in
customer
targe0ng,
opera0onal
efficiency
and
fraud
detec0on.
Playphone,
a
leading
global
mobile
entertainment
and
media
company,
enables
customer
analy8cs
for
large
scale
marke;ng
campaigns
using
advanced
predic8ve
models
on
Sybase
IQ
-­‐
crunching
through
customer
behavior,
purchasing
history,
and
many
other
relevant
metrics.
“Sybase
IQ
is
a
brilliant
analy;cs
engine.
I
don’t
know
that
the
business
would
s8ll
be
here
today
without
our
Sybase
solu8ons.”
-­‐
Simon
Rose,
Director
Of
Infrastructure
© 2012 SAP AG. All rights reserved. 5

6. SAP Sybase IQ
Our Objective
Big Data Model Diversity Complex Alternatives
Deal
With
It
Find buried signal in time! Or, get buried in Data!
© 2012 SAP AG. All rights reserved. 6

7. SAP Sybase IQ
PlexQ Technology: Versatile With Multiple Options For Predictive Analytics
Method I Method II Method III
Pull Out Push Down Federated Push - Pull
KXEN
IBM
SPSS
Panopticon
SAS
R
BI/DM Tools BI Tools
Accelerated Access Drivers
Hadoop
Data Mining Tools
Embedded libraries
R
Fuzzy Logix
Visual Numerics
Full
Mesh
High
Speed
Interconnect
© 2012 SAP AG. All rights reserved. 7

8. SAP Sybase IQ
Method I: Traditional Pull To Client
Method I
Pull Out
IBM
Standard Drivers
Optimized Drivers
SPSS
SAS
R
 Pervasive Method
 Myriad Tools
Accelerated Access Drivers
 Available Skillset
Data Mining Tools
Full
Mesh
High
Speed
Interconnect
© 2012 SAP AG. All rights reserved. 8

9. SAP Sybase IQ
Overcoming Method I: Avoid Pull
Fetch
data
from
database
Compromise
on
at
least
Accuracy
one
key
area
Create
datasets
for
analy>cal
Time
consuming
process
packages
!
Could
run
into
memory
constraints
with
large
data
sets
Analyze
data
using
sta>s>cal
func>ons
Processing
Proprietary
plaMorms
make
it
very
difficult
to
embed
Time
on
proprietary
plaMorms
!
in
applica8ons
Data
Store
results
from
datasets
back
into
database
Volume
Another
8me
consuming
process
which
could
slow
!
down
the
delivery
of
results
to
end-­‐users
Generate
reports
Database Server Logic/Filtering Applied Outside Database Server Visualization
Logic/Filtering Applied Inside Database Server Visualization
© 2012 SAP AG. All rights reserved. 9

10. SAP Sybase IQ
Push down (Method II): Solution #1 with KXEN
Factory
Analysis
Models
per
month
Craftsman
Analysis
Fully automated modeling process
• Regression
• Classification
• Segmentation
200 200 2010 2015 • Time series forecasting
0 5
• Association rules
Identify key variables
KXEN
Generate Sybase IQ specific SQL code
Executive and operational reports
In-database Push down
Easy to Use  Time to Market  More Models
© 2012 SAP AG. All rights reserved. 10

11. SAP Sybase IQ
Push down (Method II): Solution #2 with Fuzzy Logix
Fuzzy Logix Libraries Executed Inside Sybase IQ Visualization
Application Algorithms
Direct
Marke0ng:
Op>mize
the
performance
of
K-­‐
Means,
Correla>on,
PorKolio
Op>miza>on,
Internet
Marke>ng
Customer
Reten>on
PCA,
Logis>c
Regression
Marke0ng
Services:
Accelerate
the
speed
of
Sparse
Matrix
Calcula>ons,
Correla>on,
model
development
for
their
clients
Euclidean
Distance
Health
Insurance:
Risk
Management
and
Client
Sparse
Matrix
Calcula>ons,
Correla>on,
Management
Teams
-­‐
Scoring
models
to
assess
Euclidean
Distance
the
quality
and
efficiency
of
care
Banking:
Risk
Management
Correla>on,
Simula>on,
Regression,
Cubic
Spline,
Matrix
Opera>ons
© 2012 SAP AG. All rights reserved. 11

12. SAP Sybase IQ
Push down (Method II): Solution #3 with Zementis
Express
Complex
Computa0ons
In
Industry
Standard
Predic0ve
Modeling
Markup
Language
(PMML),
Plug
In
Models
Close
To
data
for
execu0on
Sybase
IQ
Server
Database
SQL
Applica>ons
Bridge
Predic>ons
Universal
Plug-­‐In
PMML
UDFs
PMML
PMML
PMML
(models)
Zemen>s
PMML
Preprocessor
(models)
(models)
(convert
&
validate)
© 2012 SAP AG. All rights reserved. 12

13. SAP Sybase IQ
Push down (Method II): Solution #3 with Zementis (contd)
Delivers a wide range of model types for high performance scoring, including:
• Decision Trees for classification and regression
• Neural Network Models: Back-Propagation, Radial-Basis Function, and Neural-Gas
• Support Vector Machines for regression, binary and multi-class classification
• Linear and Logistic Regression (binary and multinomial)
• Naïve Bayes Classifiers
• General and Generalized Linear Models
• Cox Regression Models
• Rule Set Models (flat decision trees)
• Clustering Models: Distribution-Based, Center-Based, and 2-Step Clustering
• Scorecards (point allocation for categorical, continuous and complex attributes)
• Association Rules
• Also implements data dictionary, missing / invalid values handling and data pre-processing
Handles most SAS models publishable in PMML from SAS Enterprise Miner
© 2012 SAP AG. All rights reserved. 13

14. SAP Sybase IQ
Push down (Method III): Solution #1 Federated Push-Pull With “R”
SQL
Queries
C++
UDF
“R”
Server
PUSH-­‐PULL
FEDERATION:
-­‐
UDF
Bridge
Between
Sybase
IQ
and
“R”
server
-­‐
Fire
SQL
against
Sybase
IQ
that
pushes
“R”
models
embedded
in
UDFs
to
“R”
server
for
execu>on
-­‐
UDFs
pulls
results
back
for
combining
with
rest
of
SQL
query
results
in
Sybase
IQ
-­‐
Supports
most
“R”
models
© 2012 SAP AG. All rights reserved. 14

15. SAP Sybase IQ
Push down (Method III): Solution #2 Client Side Push-Pull
• Ideal for bringing together predictive
analytics computations from different
domains
QUEST Toad for Cloud
Databases
• Better performance when computation
from each domain is pushed down to
$
each branch and then pulled together
Hadoop
Sybase IQ Hive MR Job Results
Predictive Analytics
Job Results
© 2012 SAP AG. All rights reserved. 15

16. SAP Sybase IQ
Push down (Method III): Solution #3 Data Federation Push-Pull
• Ideal for combining subsets of HDFS data
with Sybase IQ data for operational analytics
• HDFS data not implicitly stored in Sybase IQ:
Fetched into Sybase IQ In-memory tables on
the fly as part of query fired at Sybase IQ
Hadoop
Distributed UDF Bridge
File System
Predictive
Queries
© 2012 SAP AG. All rights reserved. 16

17. SAP Sybase IQ
Push down (Method III): Solution #4 Query Federation Push-Pull
• Ideal for combining subsets of Hadoop
MapReduce job results with Sybase IQ data
for operational analytics
• Hadoop MapReduce results not implicitly
stored in Sybase IQ: Fetched into Sybase IQ
In-memory tables on the fly as part of query
Hadoop fired at Sybase IQ
MapReduce UDF Bridge
Jobs
Predictive
Queries
© 2012 SAP AG. All rights reserved. 17

18. SAP Sybase IQ
Push down (Method III): Solution #5 Federated Push-Pull Text Analytics
FourSquare
Twitter
Amazon
Facebook
Kapow
So[ware
BLOB
Web
Text Index
Service
(ISYS)
SAP BusinessObjects
Sybase IQ
Data Files Data Services iSYS-­‐Search
Document
Filters
1. Limit Textual Corpus
2. Extract Concepts/Entity Relationships
3. Mine Resulting Schemas
© 2012 SAP AG. All rights reserved. 18

19. SAP Sybase IQ
A Versatile Platform For Predictive Analytics
Data
Discovery
Applica>on
Modeling
Reports/Dashboards
Business
Decisions
(Data
Scien0sts)
(Business
Analysts)
(BI
Programmers)
(Business
End
Users)
Full
Mesh
High
Speed
Interconnect
Infrastructure
Management
(DBAs)
© 2012 SAP AG. All rights reserved. 19

20. SAP Sybase IQ
Summary – transform your business
• Predictive Analytics going mainstream
• Many options available
• SAP Sybase IQ has a broad and comprehensive support
THANK YOU!
Joydeep.Das@sap.com
© 2012 SAP AG. All rights reserved. 20

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