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Non obvious relationship awareness (nora)

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Non obvious relationship awareness (nora)

  1. 1. NON-OBVIOUS RELATIONSHIP AWARENESS (NORA) Sabin Nepal Roll no:23 BscCSIT 3rd Semester Nist College , Banepa
  2. 2. OUTLINE OF PRESENTATION  Introduction  History  How does NORA works?  Features  Applications  Limitation  Conclusion  References 2
  3. 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 3
  4. 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 4 Jeff Jonas
  5. 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 5
  6. 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 6
  7. 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 7
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  9. 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 9
  10. 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 10
  11. 11. APPLICATIONS (Contd…) 11 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. 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 12
  13. 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 13
  14. 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) 14
  15. 15. ANY QUERIES? 15

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