1. www.activeinsight.net
Event Stream Processing in the Cloud
ACTIVE INSIGHT
Mike Telem
Business Development

2. Table of Contents
> Background: The Digital Era
> Processing, Correlating and Aggregating Events
> Use Cases: From Behavioral Targeting to Electrical Smart Grids
> ESP in the Cloud
> Roadmap: Where is ActiveInsight headed

3.  Our world is becoming
digital…
 Cell phones, web sites, GPS devices,
cars, ads, Financial transactions,…
 RFID, industrial eq., security sensors,
border controls, medical eq.,…
 Utilities, pipelines, meters, digital
signage, home appliances,
entertainment devices, cars, …
 Applications, infrastructure, web-
services, customer data,…
 Markets, stocks, currencies, news,
wiki’s, blogs, tweets,…
 …
 Multiple events share
various perspectives
 Event stream quantity and
frequency will fluctuate
 Effective time window for
reactions is minimal
 Reaction channels may vary
 Events should be correlated
with historical data
The Digital Era

4. Building blocks of Behavioral Targeting
 Event Stream Processing:
 Processing application level events
in a distributed environment
 Event Correlation – Directing
multiple event streams based on
their context to the corresponding
ESP containers
 Complex Event Processing:
 Processing multiple events to detect
meaningful patterns using
correlation, aggregation and time-
frames
 Pattern detection: Detecting
specific event combinations and
patterns in contexts
 Cross-Context Correlation:
Processing multiple streams into
multiple contexts / perspectives (fraud /
marketing)
 Aggregation: Accumulating correlated
events into time-based contexts, support
for “event state machine” aggregation.
 Data Integration: Caching data
sources as “reference data” for
processing
 Reaction: Invoking an action after a
successful event or pattern match

5. Different Use-cases > Similar Challenges
 Online Gaming : Real-time BI, money
laundering, local compliance, application
offload
 Online Advertisement: Behavioral
targeting, multiple site click-stream
correlation
 Ecommerce : Identifying customer
interests (up-sell/cross—sell) , Improving
conversion rates, anonymous user hooking,
campaign management
 Online Self-Service : Identifying
customer turnover or dissatisfaction,
Monitor user experience and assist in
transaction completion
 Algo-Trading : performance and
availability improvements and HW cost
reduction
 Auditing: Feeding “Who” did “What” and
“When” to auditing and SIEM systems
 Fraud detection: Fraudulent behavior
pattern detection, Bot detection, alongside
fraud detection systems
 Electrical smart-grid: Detecting misuse,
mal-functions, on-demand supply
 Home Land Security: Enhance airport
and border security, correlate multiple
events, intelligence data and incoming
alerts
 Traffic management: Vehicle location
management for Insurers, authorities and
drivers
 …Similar Challenges

6. React
Different Use-cases > Similar Challenges
Match
Correlate
Process
Aggregate

7. Behavioral Targeting in the Cloud
 Elastic
 On-demand usage
 Scaling up and out to varying
event frequencies
 … as a service
 Offloading event processing
 Dynamic Stream Sources
 Dynamic event sources
 Handling remote event sources
 SaaS Enabler
 Porting event-oriented
applications to the cloud
 SaaS component
 Enhance SaaS applications
 Offload the core application
 Comply to regional regulations
 Provide SaaS Application
integration
 IaaS/Hosting
 Value Added Services (Security,
Auditing, BI)
 Customer Experience
Management

8. ActiveInsight
Distributed Behavioral Targeting Platform
 Real-time event processing
 Multi-source event stream processing
 Event correlation and aggregation
 Pattern matching
 Integrated data caching
 Embeddable framework
 Scalable, elastic cloud run-time

9. Process
Correlate
Aggregate
Match
React
Sample Architecture
AI Server Node
Distributed Cache
Reference Data
Context
Process Match React
Context
AI Server Node
Distributed Cache
Reference Data
Context
Process Match React
Context
AI Server Node
Distributed Cache
Reference Data
Context
Process Match React
Context
Contexts
Marketing
Security
Web App
Mobile Device
Car GPS

10. Unique Value Proposition
 Embeddable, Real-time data stream processing
 Flexible and dynamic pattern definition/detection
 SpringSource development platform interoperability
 Real-time, pattern-based logic invocation
 Business driven behavior detection
 User-centric actionable events
 Real-time, value-based event feeds & user interactions
 Non-intrusive deployment
 Support for extreme transaction rates
“With ActiveInsight organizations can identify up-sell and cross-sell opportunities, react to potential customer
churn in time to prevent it, improve online self-service to customers and detect potential fraudulent activity in
real-time “

11. Q&A
Thank you!
http://www.activeinsight.net
info@activeinsight.net

12. www.activeinsight.net