WSO2 Machine Learner takes data one step further, pairing data gathering and analytics with predictive intelligence: this helps you understand not just the present, but to predict scenarios and generate solutions for the future.

1.
WSO2
Machine
Learner
1.1.0

2. WSO2
Analy+cs
Pla/orm
WSO2
Analy5cs
Pla8orm
uniquely
combines
simultaneous
real-­‐
2me
and
batch
analysis
with
predic2ve
analy2cs
to
turn
data
from
IoT,
mobile
and
Web
apps
into
ac5onable
insights
2

3. WSO2
Analy+cs
Pla/orm
3

4. WSO2
Advantages
4

5. Highly
Pluggable
Architecture
5

6. Toolboxes
for
Extensibility
6
+
Toolboxes
=
Industry
or
domain
specific
analy7cs
Toolboxes:
• Fraud
and
Anomaly
Detec+on-­‐
Supports
fraud
and
anomaly
detec7on
through
sta7c
rules,
Markov
chains,
and
scoring.
• GIS
Data
Monitoring
-­‐
Can
take
any
data
stream
tagged
with
geographical
loca7ons
and
support
visualiza7ons
of
that
data
in
a
map.
• Ac+vity
Monitoring-­‐
Lets
users
correlate
events
related
to
the
same
transac7on
in
order
to
visualize,
analyze,
and
write
queries
on
top
of
those
ac7vi7es.

7. Edge
Analy+cs-­‐Mobile
and
IoT
Streams
7
Event
correla2on/filtering
available
at
the
edge

8. High
Level
Languages
• For
both
batch
and
real-­‐7me,
we
provide
structured
,
SQL-­‐like
query
languages.
• No
Java
programming
is
required
• Lowers
the
adop7on
entry
point.
• Batch
analy7cs
relies
on
SparkSQL.
• Real
Time
analy7cs
implemented
through
WSO2
owned
solu7on
Siddhi
8

9. Real+me
analy+cs
with
Siddhi
• ThroRling
&
Blacklis7ng
users
define
stream
RequestStream
(
correla7onID
string,
serviceID
string,userID
string,
tear
string,
requestTime
long,
...
)
;
define
table
BlacklistedUserTable(userID
string,7me
long,requestCount
long);
from
RequestStream[tear==‘BRONZE’]#window.7me(1
min)
select
userID,
requestTime
as
7me,
count(correla7onID)
as
requestCount
group
by
userID
having
up
requestCount
>
5
insert
into
BlacklistedUserTable
;
9

10. Batch
Analy+cs
with
Spark
SQL
create temporary table product_data using carbonanalytics
options (schema …)
create temporary table products using carbonanalytics
options (schema …)
insert into products select product_name from product_data
group by …
10

11. Case
Studies
1

12. Smart
Home
• DEBS
(Distributed
Event
Based
Systems)
is
a
premier
academic
conference,
which
post
yearly
event
processing
challenge
(
hRp://www.cse.iitb.ac.in/debs2014/?page_id=42)
• Smart
Home
electricity
data:
2000
sensors,
40
houses,
4
Billion
events
• We
posted
fastest
single
node
solu7on
measured
(400K
events/sec)
and
close
to
one
million
distributed
throughput.
• WSO2
CEP
based
solu7on
is
one
of
the
four
finalists
(with
Dresden
University
of
Technology,
Fraunhofer
Ins7tute,
and
Imperial
College
London)
• Only
generic
solu7on
to
become
a
finalist
12

13. Healthcare
Data
Monitoring
• Allows
to
search/visualize/analyze
healthcare
records
(HL7)
across
20
hospitals
in
Italy
• Used
in
combina7on
with
WSO2
ESB
• Custom
toolbox
tailored
to
customer’s
requirement
(
to
replace
exis7ng
system)
•
13

14. Cloud
IDE
Analy+cs
• Custom
solu7on
created
in
partnership
with
Codenvy
to
bring
analy7cs
to
Codenvy
management
team
and
its
customers
• Developed
in
less
than
a
month,
with
a
custom
plug-­‐in
to
MongoDB.
• Deployed
in
the
codenvy.com
plamorm.
14

15. Addi+onal
Customers
Use
Cases
• Cisco
(BAM
+
CEP)
-­‐
OEM,
Healthcare,
Parking
Monitoring
(see
Solu7on
paRerns
based
approach
to
rapidly
create
IoE
solu7ons
across
industries,
• hRp://us14.wso2con.com/videos/#Coumara-­‐Radja
• Used
by
a
Large
Scale
IoT
System
Provider
for
use
cases
including
Vehicle
tracking,
Smart
City,
Building
Monitoring
(CEP)
• See
“Internet
of
Big
Things:
The
Story
of
Pacific
Controls,
hRp://us14.wso2con.com/videos/#Sajaad-­‐Chaudry”
• Transac7on
Monitoring
in
a
Large
Bank
(CEP)
• Knowledge
Mining
and
tracking
Prospec7ve
Customers
through
Natural
Language
data
sources
(CEP)
• CEP
Embedded
in
edge
Devices
• See
WSO2Con
2013
-­‐
Keynote:Emerging
Founda7ons
of
Next-­‐Genera7on
Business
Systems
hRps://www.youtube.com/watch?v=7CyG3JKUxWw
• ThroRling
and
Anomaly
Detec7on
by
Group
of
Telecom
Companies
15

16. WSO2
Machine
Learner
(Technical
Overview)
1

17. WSO2
Machine
Learner
17

18. Overview
18
o Open source Machine Learning (ML) tool
o Scalable way to perform machine learning
o Visually explore uploaded data sets
o Support for various machine learning algorithms
o Metrics to evaluate and compare built ML models.
o Ability to export ML models
o Extensions for real-time predictions
o REST API to expose all features i.e. ML jobs are scriptable

19. Func+onality
19
o Manage and explore your data
o Analyze the data using machine learning algorithms
o Build machine learning models
o Compare and manage generated machine learning models
o Predict using the built models

20. Manage
Data
set
20
o Supported data sources
o CSV/TSV files from local file systems.
o Files from HDFS.
o Tables from WSO2 Data Analytics Server
o Supports data set versioning.
o Version data collected overtime from the same data set
o Generate models from the different versions.
o Manage datasets based on projects ,users.

21. Pre-­‐process
&
Explore
Data
21
o Find key details from feature set
o Scatter plots to understand
relationship between feature set
o Supported graphs:
o Scatter plots, Parallel sets,Trellis
charts, Cluster diagram, Histogram
o Missing value handling with
mean imputation and discard

22. Analysis
with
ML
Algorithm
22
o Supports deep learning
o Supports supervised and unsupervised learning.
o Includes algorithms for numerical prediction, classification
and clustering.
o Supports anomaly detection algorithm.
o Supports recommendation with Collaborative Filtering
Recommendation Algorithm

23. Analysis
with
ML
Algorithm
23
o Includes algorithms for numerical prediction, classification
and clustering.
Numerical
prediction
Linear Regression, Ridge
Regression, Lasso Regression
Classification Logistic Regression, Naive Bayes,
Decision Tree, Random Forest and
Support Vector Machines
Clustering K-Means

24. Model
Evalua+on
&
Comparison
24
o Evaluate generated models
based on metrics
o Accuracy
o Area under ROC curve
o Confusion Matrix
o Predicted vs. Actual graphs
o Feature importance
o Compare models generated
from different analysis.
o Set fractions for training data

25. Integra+on
of
ML
Models
25
o Models can be used via
main transaction flow
(WSO2 ESB) or data
analysis flow (WSO2 CEP)
o Supports PMML for
interoperability.

26. Deployment
Op+ons
26
o Stand alone mode
o With external Spark
Cluster
o With WSO2 DAS as
external Spark Cluster

27. Run
Yourself
or
let
WSO2
Run
it
for
you
27
Self-Hosted
• Your operations team maintains the
deployment with production support from
WSO2
WSO2 Managed Cloud
• WSO2 Operations team runs the
deployment in a dedicated environment in
AWS datacenter of your choice
• Includes monitoring, backups, patches,
updates
• Financially backed SLA on uptime and
response time

28. Thank
You!
Download
WSO2
Machine
Learner
at:
h]p://wso2.com/products/machine-­‐learner/