In this PowerPoint presentation, you will learn how financial services leverage graph analytics tools to augment these critical categories: - Smart trading and alpha generation - Regulation and compliance - Cybercrime prevention and detection

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R E A L - T I M E A N A L Y T I C S
U S I N G S P A R K A N D
O B J E C T I V I T Y ’ S
T H I N G S P A N
B Y O B J E C T I V I T Y I N C .

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O B J E C T I V I T Y ’ S P E D I G R E E
• Headquartered in Silicon Valley since 1988
• Pioneer in high-performance distributed object data technology
• Decades of experience in “beyond petabyte” data volumes
• Deep domain expertise in massively scalable graph analytics
• Software validated and proven by Global 1000 customers and partners

3. W H Y G R A P H ?
• Use actual relationships in addition
to statistical correlation
• Ultra-fast navigation and path finding
without joins
• Combine conventional and graph
analytics to support advanced
pattern finding

4. • In-Memory graph limited by RAM
and machine
• Billions of nodes and edges require
parallelism and a distributed graph
S C A L I N G G R A P H S

5. F I N A N C I A L U S E C A S E S
• Smart Trading (alpha generation, portfolio optimization)
• Regulation and Compliance (Know Your Customer)
• Cybercrime Prevention and Detection (security breaches)

6. • Uses: Alpha generation; portfolio
optimization
• Data sources: Financial accounts,
markets, sectors, exchanges,
reference data, social media
• Opportunities:
• Compare streaming data to
historical trends
• Determine relationships between
transactions to forecast stock
value
S M A R T T R A D I N G

7. • Uses: Know Your Customer; detect
insider trading and securities fraud
• Data sources: Financial accounts,
emails, SMS, social media
• Opportunities:
• Accurately understand risk
• Prevent loss of revenue due to
rogue activities and fines
R E G U L A T I O N & C O M P L I A N C E

8. • Uses: Identify unusual patterns
indicative of security breaches
• Data sources: Network logs (firewall,
proxy, VPN, DNS), emails, HR data
• Opportunities:
• Correlate data from security and
network solutions with internal
and semantic web apps
• Be proactive, not reactive
C Y B E R C R I M E P R E V E N T I O N

9. • ThingSpan is an massively scalable distributed platform purpose-built
for real-time graph analytics and relationship discovery
• ThingSpan is architected to integrate and leverage major open source
technologies – HDFS, YARN, Spark, Kafka
• ThingSpan supports a mixed workload environment with high-speed
ingest and parallel querying

10. P O S I T I O N I N G
A N A L Y T I C S
A F T E R - T H E - F A C TS T R E A M I N G
P L A T F O R M
I N - T I M E
Time to Production
Time to I
nsight

11. T I M E - T O - I N S I G H T C O N T I N U U M
Real-time insight as
events happen
• ThingSpan + Spark
Streaming
In-time context
involving streams
and state
• ThingSpan + Graph
Exploration
After the fact insight
involving context and state
• ThingSpan + Spark Pattern
Finding
• Long-term insights

12. T H I N G S P A N A R C H I T E C T U R E

13. THINGSPAN STACK

14. D I S T R I B U T E D P R O C E S S I N G &
D A T A B A S E
Hadoop Distributed File System
Distributed from top to bottom

15. T H A N K S F O R R E V I E W I N G !
Objectivity’s ThingSpan
• Real-time graph analytics
• Apache Spark-enabled
• Hadoop (HDFS)-ready
CONTACT US:
Headquartered in San Jose, CA
Contact Us: 408-992-7100
http://www.objectivity.com