The document provides an overview of WSO2 Data Analytics Server, highlighting its capabilities in creating real-time, intelligent, and actionable business insights through a variety of analytics patterns and solutions. It details the architecture, features, and market recognition of the server, emphasizing its performance in streaming analytics and incremental processing. Various applications across industries such as banking, e-commerce, and healthcare are discussed, showcasing how WSO2 can aid in fraud detection, risk management, and operational efficiency.

1. Analytics Patterns for
Your Digital Enterprise
Sriskandarajah Suhothayan (Suho)
Associate Director/Architect
WSO2

2. How do you like your boss to be ?

3. How do you like your boss to be ?
You are Fired !!!
Without Analytics !

4. We will make your Analytics
Great Again !!!

5. We will cover
• Introduction to WSO2 Data Analytics Server
• Analytics Patterns
• Smart Analytics Solutions

6. Smart Analytics
Creating realtime, intelligent,
actionable business insights,
and data products

7. WSO2 Data Analytics Server
Realtime Incremental Intelligent

8. WSO2 DAS Architecture

9. Market Recognition
Named as a Strong Performer in The Forrester Wave™: Big Data
Streaming Analytics, Q1 2016.
• Highest score possible in 'Acquisition and Pricing' criteria
• Second-highest scores in 'Ability to execute' criteria
The Forrester Report notes…..
“WSO2 is an open source middleware provider that includes a full spectrum of
architected-as-one components such as application servers, message brokers, enterprise service
bus, and many others.
Its streaming analytics solution follows the complex event processor architectural
approach, so it provides very low-latency analytics. Enterprises that already use WSO2
middleware can add CEP seamlessly. Enterprises looking for a full middleware stack that
includes streaming analytics will find a place for WSO2 on their shortlist as well.”

10. Receivers
+ Pluggable
Custom Receivers

11. Event Streams
Event Stream Schema
Name : TemperatureStream
Version : 1.0
Attribute Type
sensorID String
temperature double
preasure double
Event
StreamID TemperatureStream:1.0
Timestamp 1487270220419
sensorID AP234
temperature 23.5
preasure 94.2
SourceIP 168.50.24.2
+ Support for
arbitrary key-value
pairs

12. Realtime Processing
• Process events in streaming fashion (one event at a time)
• Processing topology (Execution Plan)
– Written in Siddhi Query Language
– Runs in isolation
– Include
• Queries
• Input event streams
• Output event streams

13. Realtime Processing Patterns
• Transformation
– projection, transformation, enrich, split
• Temporal Aggregation
– basic stats, group by Aggregation, moving averages
• Alert and Threshold
• Event Correlation
• Trends
– detecting rise, fall, turn, triple bottom
• Partition
• Join Streams
• Datastore Querying

14. Siddhi Query Syntax
define stream <event stream>
(<attribute> <type>,<attribute> <type>, ...);
from <event stream>
select <attribute>,<attribute>, ...
insert into <event stream> ;

15. Basic Patterns
define stream SoftDrinkSales
(region string, brand string, quantity int, price double);
from SoftDrinkSales[price >= 100]#window.time(1 hour)
select region, brand, avg(quantity) as avgQuantity
group by region, brand
having avgQuantity > 1000
insert into HighHourlySales ;
Temporal Aggregation,
Transformation,
Threshold & Filtering
Other supported window types:
timeBatch(), length(),
lengthBatch(), etc.

16. Event Correlation Pattern
define stream Purchase (price double, cardNo long,place string);
from every (a1 = Purchase[price < 10] ) ->
a2 = Purchase[ price >10000 and a1.cardNo == a2.cardNo ]
within 1 day
select a1.cardNo as cardNo, a2.price as price, a2.place as place
insert into PotentialFraud ;
Only Supported in CEP Systems!!!

17. Data Persistence
• Provides backend datastore
agnostic way to store and
retrieve data
• Provides standard REST API
• Pluggable data connectors
– RDBMS
– Cassandra
– HBase
– custom ...
Data Abstraction Layer
Custom

18. Siddhi Event Table and Join
@from(eventtable = 'rdbms' , datasource.name = ‘CardDataSource’ ,
table.name = ‘UserTable’, caching.algorithm’=‘LRU’)
define table CardUserTable (name string, cardNum long);
from Purchase as p join CardUserTable as c
on p.cardNo == c.cardNum
select p.cardNo as cardNo, c.name as name, p.price as price
insert into PurchaseUserStream ;
Supported for RDBMS, In-Memory,
Distributed In-Memory Grid
(Hazelcast),
WSO2 Analytics Table
Cache used to
improve performance

19. Incremental Processing Patterns
• Periodic Analysis
• Incremental Analysis
– on newly arrived data
• Lambda Architecture
• Realtime Incremental Analytics
– on newly arrived data with low latency

20. Periodic Analytics Pattern
• Runs through the full data set
• Summarize data periodically
• E.g: Identifying median
• Supported with WSO2 DAS
– Spark Script Scheduling
– Siddhi Batch windows.
https://www.hsph.harvard.edu/population-development/2014/09/08/impact-of-schedule-control-on-quality-of-c
are-in-nursing-homes/

21. Incremental Analytics Pattern
• For incremental Big Data processing
• Periodically process the newly arrived data
• Via Extended Spark
create temporary table orders using CarbonAnalytics
options (tableName "ORDERS",
schema "customerID STRING, phoneType STIRNG,
OrderID STRING, cost DOUBLE, _timestamp LONG -i",
incrementalParams "orders, 60");

22. Lambda Architecture Pattern
http://lambda-architecture.net/
Supported with Siddhi Event
Tables

23. Realtime Incremental Analytics Pattern
• Low latency and low resource utilization
• Works for both short and long term streaming data
• Enhanced version of Lambda Architecture
Realtime incremental processing
(Seconds & Minutes)
Batch incremental processing
(Hour and above)

24. Communicate
Alerts Dashboards
Interactive
Queries APIs
https://old.datacandy.com/technology/loyalty-software-api/, http://martywdamon.blogspot.com/2013/05/child-endangerment.html
http://www.stormcorp.ch/products/interactive-queries/

25. Publishers + Pluggable
Custom Publishers

26. Dashboards
• Dashboard generation
• Gadget generation
• Gather data via
– Websockets
– Polling
• Custom & Personalized
Gadget and Dashboard
support

27. Interactive Queries
• Full text search
• Drilldown search
• Near real time data indexing
and retrieval
• Powered by Apache Lucene

28. Intelligent Processing Patterns
• Build and Run ML Models
• Streaming ML
• Anomaly Detection
• Detect Rare Activity Sequences
• Scoring
• Realtime Risk Detection

29. Predictive Analytics
• Guided UI to build Machine
Learning models with
– Apache Spark MLlib
– H2O.ai (for deep learning
algorithms)
• Build with R and export them as
PMML
• Run built models against realtime
data in DAS

30. Real time Prediction
Using built machine learning models
from DataStream#ml:predict(“/home/user/ml.model”, “double”)
select *
Insert into PredictionStream ;
Or use R scripts, Regression, Markov Chains or Anomaly
Detection on realtime

31. Analytics Extensions Store
• geo: Geographical processing
• nlp: Natural Language Processing
(with Stanford NLP)
• ml: Running Machine Learning and
PMML models
• timeseries: Regression and time
series
• math: Mathematical operations
• str: String operations
• regex: Regular expression
• more ...
https://store.wso2.com/

32. Smart Analytics Solutions

33. WSO2 DAS Solutions
• Banking and Finance
– Fraud Detection & Anti Money Laundering
– Risk Management
– Stock Market Surveillance
• eCommerce and Digital Marketing
• Fleet Management
• Smart Energy Analytics
• QoS Enablement
• System and Network Monitoring
• Healthcare

34. Fraud Detection & Anti Money Laundering
Applies for
• Payment Fraud
• Money Laundering
• Identity Fraud
Ways to solve
• Static Rules
• Fraud Scoring
• Machine Learning
• Markov Models
http://www.carp.ca/2013/03/08/march-is-fraud-prevention-month/

35. Avoid False Positives with Scoring Pattern
• You just bought a diamond ring
• You bought 20 diamond rings, within 15 minutes
at 3am and shipped it to 4 global locations?
• Use combination of rules
• Give weights to each rule
• Single number that reflects multiple fraud indicators
• Use a threshold to reject transactions
Score = 2*X + 4*Y + 13*Z

36. Machine Learning Pattern
• Identify ‘unknown’ types of fraud
• Use classification techniques

37. • Model randomly changing systems
• Detect using Siddhi Markov Models
Detect Sequence of Rare Activities
https://en.wikipedia.org/wi
ki/Markov_chain

38. Investigation
Capture alert & further investigate with interactive analytics

39. Banking and Finance
Risk Management
• End of Day Risk processing is no
longer adequate
• Support Realtime Intra-day Value at
Risk computations
• Calculated using realtime
– Market prices
– Portfolio changes

40. Realtime Value at Risk
WSO2 DAS models Value at Risk using 3 standard methods
• Historical Simulation
• Variance-Covariance
• Monte Carlo Simulation
Query :
from InputSteam#var:historical(251, 0.95, Symbol, Price)
select *
insert into VaRStream ;

41. Banking and Finance
Stock Market Surveillance
Manipulation Methods
• Front Running
• Pump (and Dump)
• Insider Dealing
• Wash Trading
• Churning
• more ...

42. Banking and Finance
Stock Market Surveillance ...
Manipulated via :
• Artificially inflating or deflating stock prices
• Exploiting prior knowledge of company proceedings
• Abusing advanced knowledge of pending orders
Solved via :
Joining market data feeds with external data streams such as
company announcements, news feeds, twitter streams, etc

43. Client Front Running Detection
with Event Correlation Pattern

44. eCommerce and Digital Marketing
Recommendations
Based on :
• Customer buying history
• Item buying history
• Current trends
• Machine Learning
Customers are likely to
choose
recommendations as
they are personalized.

45. Proximity Marketing

46. Heat Map Analysis

47. eCommerce and Digital Marketing
AD Optimisations
Achieve :
• More clicks
• More conversions
• Effective use of
allocated budget
• Higher click through
ratio (CTR)
• Greater ROI

48. Fleet Management
You can know
• Where your fleet is ?
• Driving behaviour
• Are vehicles used optimally?
– Fuel expenses
– Travel time
– Round trip time
• Current situation on the road
• more ...
https://rnpc-rekos.ru/gps-fleet-management-systems/

49. Fleet Management Dashboard

50. Smart Energy Analytics
• Optimize Smart Grids
– Analyse energy demand
– Predict required energy
supply
• Understand steady state
operations
• Act on events in energy network
• Monitor process and equipments
on energy network
• more ...

51. • Monitor and manage
Equipments
• Home Automation
• Surveillance
• Maintenance
• Edge Analytics with
Siddhi
• more ...
Smart Building / Home Analytics

52. QoS Enablement, Network & System Monitoring
• Real-Time Botnet Traffic Detection
• Auto scaling based on
– CPU utilisation
– Memory consumption
– Load average
– Request count
– etc ...

53. QoS Enablement, Network & System Monitoring ...
• Throttling (E.g. In WSO2 APIM)
Multiple throttling levels
– API
– Application
– Resource URL
– Subscription level.
• Supports hierarchical throttling
limits

54. Healthcare
• HL7 Messaging support
• Monitoring Medical
Records
– Delay in patient visits
– Alerts based on
glucose levels
• Used with of WSO2
Integration (ESB)

55. Key Differentiations
• Realtime analytics at it’s best
– Rich set of realtime functions
– Sequence and pattern detection
• No code compilations - SQL Like language
• Incremental processing for everyday analytics
• Intelligent decision making with ML and more
• Rich sets of input & output connectors
• High performance and low infrastructure cost

56. Thank You!