Fast data is big data, continuously streaming in, from which information is to be learned in (near) real time. This session demonstrates how Oracle Event Processing is used to analyze live streams of data to find patterns, deviations, and aggregates. The findings are reported in the form of business events that are pushed in live dashboards to Oracle Business Activity Monitoring, which also evaluates business rules on the business events and takes action when required. Examples to be demonstrated in this session include car sensors, website traffic, Twitter feeds, and bank run detection. Oracle SOA Suite 12c, WebSockets, Oracle Application Development Framework (Oracle ADF) active data visualization tools components, and JMS are used to process, forward, and act.

1. Lucas Jellema
Oracle OpenWorld 2014, San Francisco, CA, USA
How Fast Data Is Turned into Fast Information and Timely Action

2. 2

3. Overview
•What is [special about] Fast Data?
–Continuous, Volume|Velocity|Variety, Real Time
•Challenges
–Volatile, non persistent
–Data => Information, Conclusion, Alert, Recommendation, Action
•Strategies
–Smart gathering
–Discard – filter, aggregate, pattern (and also look for missing events!)
–Promote (process, enrich)
–Visualize
•Technology/Tools
•Demonstration/Cases

4. 4
Fast Data
•Tweet
•Feed
•Beat
•Signal
•Measurement
•Message
•Mail
•Notification
•Tick
•Pulse

5. 5
New theme (that brings it all together)

6. 6
Some event producing devices

7. 7

8. Most of these events….

9. 9

10. 10
Fast Data Processing
Fast Data
Smart Processing
•Information
•Conclusion
•Alert
•Recommendation
•Action

11. 11
Fast Data Processing Multi-stage cleansing & aggregation
Fast Data
Smart Processing
•Information
•Conclusion
•Alert
•Recommendation
•Action

12. 12
Typical Flow and Additional Challenge…
Business event
Business Value
Data captured
Analysis completed
Action taken
Fragmented event entities
TIME

13. 13
The V-factor
Volume
Velocity
Variety
VALUE

14. 14
Key strategy
•Discard – as early as possible (close to the source)
–Ignore irrelevant events
–Filter out unneeded attributes
–Takes samples instead of entire stream
–Aggregate: merge multiple events into one

15. 15
Fast Data Processing: Oracle Event Processor
Fast Data
Smart Processing
Oracle Event Processor
RMI File REST HTTP Channel JMS Database Custom (Java) SOA Suite EDN Coherence JMX QuickFix (financial)
RMI File REST HTTP Channel JMS Business Rule Database Custom (Java) SOA Suite EDN Coherence JMX

16. 16
Oracle Event Processor
•Light weight, real-time (sub-sub-second), in-memory, continuous query engine
–Available in embedded form – with corresponding licence
•Interacts with many different channels – inbound and outbound
•Has internal caches to enrich events and temporarily retain events
•Uses CQL to:
–Filter, aggregate, enrich and detect patterns (including missing events)
events
Event Processor

17. Input Adapter
Channel
Input Adapter
Channel
CQL Processor
CQL Processor
Channel
CQL Processor
Channel
Channel
Output Adapter
Output Adapter
OSGi Bundle/Spring Application Context
DB
Input adapters connect to data sources
Channels help control the flow of data and can be tuned for optimal performance
Databases and Coherence caches can be referenced directly in CQL processors
CQL processors contain correlation, aggregation and pattern matching business logic
Output adapters send data and alerts to downstream systems and business processes
Coherence
Oracle Event Processing Application

18. 18
Fast Data Processing Fusion Middleware Tooling
Fast Data
Smart Processing
Oracle Event Processor
Coherence
SOA Suite 12c EDN
RMI
File
REST
HTTP Channel
JMS
Database
JMX
Custom (Java)
RMI
File
REST
HTTP Channel
JMS
Business Rule
JMX
Database
Custom (Java)

19. 19
Fast Data Processing Fusion Middleware Tooling
Fast Data
Smart Processing
•Information
•Conclusion
•Alert
•Recommendation
•Action
OEP
BAM
ADF
Coherence
SOA Suite
EDN
BPMSuite
BPEL
Task
BI
RTD
ODI
Golden Gate
NoSQL

20. 20
Fast Data Example
14,0
16,1
14,1
16,1
16,0
13,1
14,0
16,0
13,1
13,0
14,1
16,0
14,1
13,0
14,1
16,0
13,1
14,0
Smart Processing
Oracle Event Processor

21. 21
Demonstration: Live Tennis
•Tennis Tournament
•Many matches played in parallel
•The data that is produced:
–At a rate of up to 10 events/minute
Match Id, Player [who scored]
14,0
16,1
14,1
16,1
16,0
13,1
14,0

22. 22
Demonstration: Live Tennis
•The information, conclusions & actions we are looking for:
–Scoreboard per game, set, match
–Match start and completion (action: inform next players for that court)
–Interrupted match (action: go and check out the reason for the interruption)
Fast Data
Smart Processing
•Scoreboard
•Match start and completion
•Interrupted match

23. 23
OEP application to process fast tennis data
•Preparation
–Define event definitions
–Create local, in memory cache with static, enriching data
–Gather (in this case generate) tennis data through adapter
–Create Event Sink to consume all findings and publish to console
TennisMatchEvent
matchId
player

24. 24
Simple Time-slice Aggregation
•Produce aggegrates once every 30 seconds
–Count number of matches going on currently (meaning: in the last 30 seconds)
–Calculate average time per rally (over the last 30 seconds)
–Count total number of points played (over the last 30 seconds)

25. 25
Simple Time-slice Aggregation combined with all-time findings
•Produce aggegrates once every 30 seconds – partially over last 30 seconds and partially over ‘all time’
–Count number of matches going on currently (meaning: in the last 30 seconds)
–Calculate average time per rally and count total number of points played (all time)
–Longest rally played in the tournament

26. 26
Simple Time-slice Aggregation combined with all-time findings

27. 27
Match Level events

28. 28
Rally’s to games
-The first player to have won more than 4 points
-and have won two or more points more than his opponent
TennisMatchEvent
matchId
player

29. 29
Games to Sets
-The first player to have won more than 5 games
-and have won two or more games more than his opponent

30. 30
Detect interrupted matches by ‘finding’ missing events
•When a match is interrupted, obviously no more ‘rally point events’ are produced
•Detecting the absence of these events for a match [that has begun] is equivalent to detecting an interruption of the match
–Unless the match is complete because someone won

31. 31
Detect interrupted matches by ‘finding’ missing events

32. 32
Complete EPN diagram for Tennis Tournament Processor
•A single OEP application that consumes fine grained rally point events and performs three-stage aggregation and enrichment
TennisMatchEvent matchId player
New Match
Match Finish
Interrupted
Match
Set Won
Game
Won

33. 33
Demonstration: Car Parks Management
Fast Data
•Available lots
•Average parking time
•Tow-candidates (abandoned cars)
Smart Processing

34. 34
Car Parks Management
Fast Data
•Available lots
•Average parking time
•Tow-candidates (abandoned cars)
Smart Processing
Car Entry
- CarParkId
- Licence Plate
Car Exit
- CarParkId
- Licence Plate
CarparkCapacityAlert - CarParkId - % full
AbandonedCar - CarParkId - Licence Plate
CarStayDone - CarParkId
-Licence Plate
-Duration
CarParkStatus - CarParkId
-#cars
-Avg stay

35. 35
Credit Card Theft Detection
•Several situations in the past
–Credit card is stolen in the main terminal building
–Several purchases are made in shops on the way from that area to the main exit
•Purchases between $200-$500 dollar
•Purchases made within 5 minutes of each other
•Sometimes the purchases are made in not entirely the direct route to the exit
EXIT
Main Terminal

36. 36
Credit Card Theft Detection
•Several situations in the past
–Credit card is stolen in the main terminal building
–Several purchases are made in shops on the way from that area to the main exit
•Purchases between $200-$500 dollar
•Purchases made within 5 minutes of each other
•Sometimes the purchases are made in not entirely the direct route to the exit
•To catch the perpetrator
–Consume the credit card purchase event stream for airport shops
–Spot situations where three or more purchases of $200-$500 are made within 5 minutes from each other and roughly in the terminal => exit physical order
–Publish an event to alert security staff
•To watch for any further purchases with that credit card
•To inform show staff for that credit card
•To send staff to the exit to try and apprehend the thief (perhaps based on the shopping bags he is carrying from the shops he bought stuff at)

37. 37
Catch me if you can
EXIT
Main Terminal

38. 38
Catch me if you can
EXIT
Main Terminal
$440
$300
$380
$250

39. 39
Toilet Cleanliness
•Every toilet facility has a customer satisfaction station
–Every 30 seconds, someone can indicate (1-4) their satisfaction
•All entries are collected – signals (toiletId, rating)
•When the rating < 3.3 (average over last 5 signals) – the cleaning staff has to act
•When the rating < 3 and the
•previous signal for that toilet is longer ago than 5 minutes, then also action is required

40. 40
Human consumers
•Slow at data processing
•Not electronically connected
•Visually oriented (1 picture > 1000 words)
•Frequently (though perhaps decreasingly so) the actor or decision maker
•Interact along human communication channels
•Use visualization to present findings, conclusions, recommended actions
–And as a second tier of fast data processing: Highlight (filter), aggregate, patterns, extrapolate/interpolate, missing elements
•Sometimes take over from humans and just take action

41. 41
Audience Challenge

42. 42
Audience Challenge – 1/2

43. 43
Audience Challenge – 2/2

44. 44
Visualize and Aggregate

45. 45
Trends and Extrapolation

46. JMS
HTTP
JMX
File
DB
RSS
EDN
…
OEP
SOA Suite
BAM
E DN
JMS
@
Alerts
(rules)
Reports
HTTP Consumers
Web
Application
The OEP to Human interface

47. 47
Real Time – from Event to Task OEP => SOA Suite 12c EDN
Fast Data
Smart Processing
Oracle Event Processor
SOA Suite 12c
EDN
BPEL
Task
BPMN
Medi- ator
event
event
event

48. 48
Real Time – from event to UI
Fast Data
Smart Processing
Oracle Event Processor
WebLogic
JMS
event
msg
WebSocket
Server
msg
msg

49. 49
Real Time – from event to UI Business Activity Monitoring
Fast Data
Smart Processing
Oracle Event Processor
WebLogic
JMS
event
msg
BAM
msg

50. 50
Summary
•Fast Data (events): Vast, Continuous, Velocity, Variety
–Wanted: Near real time conclusions, recommendations, alerts, actions
•Strategy:
–Discard – as early as possible (Filter, Aggregate)
–Enrich, Pattern Match, Missing Events, Retain, Publish higher level, more coarse grained business event
–Repeat this cycle multiple times (such as rally point, game, set, match)
•Technology for Fast Data processing: Oracle Event Processor & CQL
–Interacts with JMS, EDN, RMI, HTTP (/REST), JMX, Database, Coherence
•To assist humans in Fast Data and Information Processing: Visualization
–Filter, Aggregate, Enrich, Pattern Match (1 picture > 1000 words)
–Technology: BAM (Dashboard and Rule processing), ADF Data Visualization
–Also: turn findings into actions using Human Task, BPEL and BPM via the SOA Suite 12c Event Delivery Network (EDN)