The document discusses WSO2 Machine Learner 1.1.0, an open-source analytics platform that integrates real-time and batch analysis with predictive capabilities tailored for IoT, mobile, and web applications. It includes features such as customizable toolboxes for various domains, support for multiple machine learning algorithms, and the ability to generate real-time predictions via REST APIs. Case studies highlight successful applications in smart home energy monitoring, healthcare data analysis, and other industries, showcasing WSO2's scalable and extensible architecture.

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/