Non-Obvious Relationship Awareness (NORA) is a data mining system developed by Jeff Jonas in 1989, initially for casino fraud detection, which identifies relationships between individuals and organizations using multiple datasets. It features real-time analytics and is employed across various sectors, including law enforcement and retail, although it raises privacy concerns and relies heavily on data quality. Today, NORA is recognized for its broad applicability and potential in enhancing security and fraud detection.

1. NON-OBVIOUS
RELATIONSHIP
AWARENESS (NORA)
Sabin Nepal
Roll no:23
BscCSIT 3rd Semester
Nist College , Banepa

2. OUTLINE OF PRESENTATION
 Introduction
 History
 How does NORA works?
 Features
 Applications
 Limitation
 Conclusion
 References
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3. INTRODUCTION
 System that mines data from data resources that
determines non-obvious relations between people or
organizations
 Enables users to use data to find relationships where
one ​would not think a relationship exists
 Also developed to automate and speed up the process
of sorting and analyzing large amounts of data
 First developed to identify casino frauds
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4. HISTORY
 Built by Jeff Jonas in 1989
 Jeff founded system research and development in 1983
 Brought in professional management in 2002
 Funded by CIA
 Acquired by IBM in January 2005
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Jeff Jonas

5. HOW DOES NORA WORKS?
MULTIPLE DATASETS AND PERPETUAL ANALYTICS
 spots similar entities or identities across multiple
relational databases
 Collecting the different data-sets, NORA combines the
data into a single database using XML
language(Extensible Markup Language) for real time
processing
 Uses real time intelligence system that alerts users
when a related match is made
 “perpetual analytics”-process of performing real-time
analytics on data streams
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6. HOW DOES NORA
WORKS?(Contd…)
IDENTITY RSOLUTION
 Name Standardization – gives common name replacing
nick names used
 Address verification and correction – corrects
addresses by relating it to international address
databases
 Data Quality – data rules is applied (eg -social security
number)
 Data Enhancement – such as adding lattitudes and
longitudes to the address
 Entity Resolution – this is where it is determined whether
a new or existing entity
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7. HOW DOES NORA
WORKS?(Contd…)
RELATIONSHIP RESOLUTION
 resolved identities are linked with obvious or non-
obvious relationships
 Relationships could be to individuals or organizations
and professional social or criminal
 Works by cross referencing data from other sources and
linking by identity attributes
 Identity attributes can be phone, addresses etc
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8. 8

9. FEATURES OF NORA
 Sequence neutrality :- reaction to new data as it is
added to evaluate whether it matches or do not matches
the existing data
 Perpetual analytics :- intelligence alert system notifying
the user
 Extensible :- accepts new data files and new attributes
and changing configuration files
 Real Time :- real time processing and alerting system
 Scalable :- handles large amount of data
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10. APPLICATIONS
Casinos
 To identify the suspicious activities in casino games and
finding the game frauds
Criminal Investigations
 NORA is used by homeland security ,FBI and other law
enforcement agencies to identify and arrest the
criminals
Gaming Industry
 To protect from people doing unknowing business with
others and insider threats
Retail Theft
 To find out the individuals who are involved in shoplifting
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11. APPLICATIONS (Contd…)
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OTHERS
 Determining terrorist threats
 Useful in hotel guest convenience and other customer
service alerting
 Multi-agency collaboration
 Boarder protection systems
 Visa programs
 Transportation safety

12. LIMITATIONS
 Compromisation in privacy of the people or
organizations
 Cannot recognize the type of activities ongoing either
legal or not
 May not give accurate result all the time as it is
dependent on the data resources
 Private citizens are not aware about the amount of data
that law enforcement agencies have on them and how
they are using it
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13. CONCLUSION
 software with unlimited potentials
 Origin are associated with casinos but nowadays used
in wide range in industries and various organizations
 Helps in Precise detection and identification of bad guys
 Highly scalable to hold large data
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14. REFERENCES
 http://www.uniassignment.com
 http://aconstantineblacklist.blogspot.com/2009/02/cias-
in-q-tel-ibm-non-obvious.html
 http://www.computerworld.com/article/2587381/business
-intelligence/the-search-is-on.html
 http://www.eweek.com
 http://annex.wikia.com/wiki/Nora (technology)
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15. ANY QUERIES?
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