The document discusses WSO2's big data platform and applications. It provides an overview of what can be done with big data, including optimizing processes to save money and resources, saving lives through advanced analytics of data like weather and health, and enabling new technologies through simulations. It then describes WSO2's big data architecture and analytics offerings, including the WSO2 Analytics Platform and its real-time and batch processing capabilities. Several use cases are presented, such as for smart homes, healthcare, cloud services, and more. The platform provides high-level query languages and supports multiple industries and custom extensions.

1. WSO2 Big Data
Platform and
Applications
Srinath Perera
Director, Research, WSO2 Inc.
Visiting Faculty, University of Moratuwa
Member, Apache Software Foundation
Research Scientist, Lanka Software Foundation

3. What can We do with Big Data?
 Optimize (World is inefficient)
o 30% food wasted farm to plate
o GE 1% initiative (http://goo.gl/eYC0QE )
- 1% saving in trains can save 2B/ year
- 1% in US healthcare is 20B/ year
- In contrast, Sri Lanka total exports 9B/ year.
 Save lives
o Weather, Disease identification,
Personalized treatment
 Technology advancement
o Most high tech research are done via
simulations

4. Big Data Architecture

5. Big data Processing Technologies

6. WSO2 Analytics Platform

7. Big Data Analytics Offering

8. 8
Combined Power
 Users can send
events to both BAM
and CEP via the
same APIs
 CEP can combine
output from batch
Processing and data
from various storage
(e.g. databases) with
real-time processing
o e.g. Implementing Lambda
Architecture

9. 9
Highly Pluggable Architecture

10. WSO2 CEP

11. WSO2 BAM
● Powered by Apache Hadoop with management and queries using
Apache Hive
● Parallel, distributed processing based on the MapReduce
programming model
● Runs on local Hadoop node or can be delegated to a cluster of
Hadoop nodes
● Scalable script-based analytics written using an easy-to-learn, SQL-
like query language.
Analyzer
Engine
Hadoop
Cluster
Data Store
(Cassandra/
RDBMS)

12. 1
High Level Languages
 For both batch and real-time, we provide
structured , SQL-like query languages.
o No Java programming is required
 Lowers the adoption entry point
 BAM
o Relies on Apache Hive
 CEP
o Implemented though our own solution, Siddhi.

13. 1
Event table:(Map a database as an event
stream)
Filter: (Process single
transaction)
Windows:(Track a window of events)
CEP Operators with Siddhi
 define stream RequestStream ( correlationID string, serviceID
string,userID string, tear string, requestTime long, ... ) ;
 define table BlacklistedUserTable(userID string,time long,requestCount
long);
 from RequestStream[tear==‘BRONZE’]#window.time(1 min)
 select userID, requestTime as time, count(correlationID) as
requestCount
 group by userID
 having up requestCount > 5
 insert into BlacklistedUserTable ;

14. 1
Smart Home
 DEBS (Distributed Event Based Systems) is a
premier academic conference, which post
yearly event processing challenge
(http://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 solution
measured (400K events/sec) and close to one
million distributed throughput.
 WSO2 CEP based solution is one of the four
finalists (with Dresden University of
Technology, Fraunhofer Institute, and Imperial
College London)
 Only generic solution to become a finalist

15. 1
Healthcare Data Monitoring
 Allows to search/visualize/analyze healthcare
records (HL7) across 20 hospitals in Italy
 Used in combination with WSO2 ESB and BAM
 Custom toolbox tailored to customer’s requirement
( to replace existing system)


16. 1
Cloud IDE Analytics
 Custom solution created in partnership
with Codenvy to bring analytics 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 platform.

17. 1
Watch at:
https://www.youtube.com/watch?v=nRI6buQ0NOM
Case Study: Realtime Soccer Analysis

18. 1
Additional Customers Use Cases
 Used in Healthcare, Parking Monitoring (see Solution patterns based
approach to rapidly create IoE solutions across industries,
o http://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)
o See “Internet of Big Things: The Story of Pacific Controls,
http://us14.wso2con.com/videos/#Sajaad-Chaudry”
 Transaction Monitoring in a Large Bank (CEP)
 Knowledge Mining and tracking Prospective Customers through Natural
Language data sources (CEP)
 CEP Embedded in edge Devices
o See WSO2Con 2013 - Keynote:Emerging Foundations of Next-
Generation Business Systems
https://www.youtube.com/watch?v=7CyG3JKUxWw
 Throttling and Anomaly Detection by Group of Telecom Companies

19. 1
Extensions and Toolboxes
 Fraud and Anomaly Detection Toolbox - ( Static Rules, Statistical
outliers, Markov Chains)
 Time Series Toolbox
 Natural Language Processing Plugin (Entity Extraction, POS tagging,
Sentiment analysis)
 GIS Toolbox (Geo Fencing, Tracking, Speed Alarms)
 Running machine learning models exported as PMML with CEP (e.g.
from R)
 Video Monitoring with OpenCV
 For more info, http://wso2.com/library/articles/2014/08/wso2-cep-in-
action-an-analysis-of-use-in-real-world-applications-of-different-
domains/

20. 2
Geo Fencing and Tracking Toolbox

21. 2
SolidCon Demo -
http://wso2.com/library/articles/
2014/09/demonstration-on-
architecture-of-internet-of-
things-an-analysis/
IoT Demos and Use Cases
 IOT Reference Architecture,
http://wso2.com/landing/internet-of-
things-uk-2014/
 Internet of Big Things: The Story of
Pacific Controls,
http://us14.wso2con.com/videos/#Saj
aad-Chaudry
 Federated Identity for IoT with
OAuth,
http://www.infoq.com/presentations/f
ederated-identity-IoT-OAuth

22. 2
Analyzing sentiments for
FIFA twitter hashtag
Sentimental Analysis Demo

23. Work in Progress

24. 2
Predictive Analytics

25. 2
Leveraging Apache Storm in CEP

26. 2
BAM Enhancements
 Work underway to Switch to Apache
Spark and Shark SQL like Queries
support in BAM
o Faster Queries
o Keeping SQL like language
 Use “Hive on Spark” for migration
purposes
 Lower the adoption point of BAM by
packaging by default an RDBMS instead
of Cassandra.
o Architecture already scales from small
deployments to BigData

27. Questions?

28. 2
Business Model