Authority Miner® is a visual analytical tool that enables end users, business analysts and data miners to build business processes that can be subsequently automated to provide up to date, timely and accurate information to decision makers within the organisation.

Visual Analytics

The intuitive graphical workflow user interface readily supports the ability to access, prepare, analyse and model data from a wide variety of sources. Once built, the business processes can be easily altered to accommodate changes in business requirements.

Automated Processes

Within Authority Miner® business processes can be established by combining data from one or more of the organisation's systems. Thereafter, these processes can be re-run on new data which will provide up to date, actionable information and intelligence.

Save Time and Money

Each business process can be stored centrally to be used throughout the entire organisation ensuring consistency in the preparation of operationally effective and actionable intelligence information and documentation. The processes are easy to create and, therefore, easy to alter as the dynamics of the organisation changes.

To see how Authority Miner® improves performance management, click here

To check our services for policing intelligence, click here

Benefits

 · Build processes to match business requirements
 · Each stage of the building process can be verified

Features

 · Import/export i2
 · Combine data on the desktop from multiple sources
 · Only tool in its class to have full audit capabilities making it suitable for confidential/secure
   environments
 · Data can be imported from and exported to:
   ORACLE
   SQL Server
   ODBC Sources
   Microsoft Excel spreadsheets
   Text files
   Comma delimited files
   Microsoft Word
   PDF files

Recommended System requirements:

Windows XP/Vista/7 32 bit & 64 bit
2GB RAM
50GB DiskSpace
Authority Miner® will interface with i2 and iBase

 
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Privacy Policy  

Privacy Statement

A E Solutions (BI) Ltd. is dedicated to respecting and protecting your privacy. We will not release any contact or account information from our clients, including e-mail address, without prior consent. If you have any questions regarding this policy, please contact us.

This policy was last modified on 11 April 2011

References  

1. ADDERLEY, R., MUSGROVE, P. B., (1999), Data mining at the West Midlands Police: A study of bogus official burglaries. BCS Special Group Expert Systems, ES99, London, Springer-Verlag, pp 191-203.

2. ADDERLEY, R., MUSGROVE, P. B., (2001), General review of police crime recording and investigation systems. A user's view. Policing: An International Journal of Police Strategies and Management, 24(1) pp 110-114.

3. ADDERLEY, R. & MUSGROVE, P.B. (2001), Data mining case study: Modelling the behaviour of offenders who commit serious sexual assaults, ACM Special Interest Group on Knowledge Discovery and Data Mining (2001) Proceedings: seventh ACM SIGKDD international conference on knowledge discovery and data mining, 2001, August 26-29 2000, (San Francisco). Association for Computing Machinery Inc. pp 215-220.

4. ADDERLEY, R. & MUSGROVE, P.B. (2003), Modus operandi modelling of group offending: a data mining case study, The International Journal of Police Science and Management 5(4) pp267-276.

5. ADDERLEY, R. (2003), The use of data mining techniques in active crime fighting, International conference on computer, communication and control technologies and the 9th international conference on information systems analysis and synthesis, 2003, 31 July – 3 August 2003. (Orlando): CCCT. pp356-361.

6. ADDERLEY, R (2005), The use of data mining techniques in operational crime fighting in ZURADA, J., KANTARDZIC, M., (eds) New Generations of Data Mining Applications. pp 525-543, Wiley & Sons, New Jersey

7. ADDERLEY, R., (2004), Using data mining techniques to solve crime, 2nd Symposium on Intelligence and Security Informatics, 2004, 10-11 June. (Tucson) JCDL 2004. pp 418-425.

8. ADDERLEY, R., BOND, J., TOWNSLEY, M. (2006), The use of data mining techniques to model crime scene investigator performance, Proceedings of AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2006, 11 – 13 December 2006 (Cambridge), London, Springer-Verlag, pp 19-32.

9. ADDERLEY, R., BOND, J., TOWNSLEY, M. (2006), The use of data mining techniques to model crime scene investigator performance, Knowledge Based Systems 20(2) pp 170-176.

10. ADDERLEY, R., BOND, J., TOWNSLEY, M. (2006), Predicting Crime Scene Attendance, The International Journal of Police Science and Management V9(4) pp 312-323.

11. GROVER, V., ADDERLEY, R., BRAMER, M. (2006), Review of crime prediction techniques. Proceedings of AI-2006, the Twenty-sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2006, London, Springer-Verlag, pp 233-237.

12. ADDERLEY, R., BOND, J. (2008), The Effects of Deprivation on the Time Spent Examining Crime Scenes and the Recovery of DNA and Fingerprints, Journal of Forensic Science 53(1) pp 178-182.

13. ADDERLEY, R., BOND, J., (2007), Police Forensic science performance indicators – a new approach to data validation Proceedings of AI-2007, the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI-2007, London, Springer-Verlag, pp 163-174.

14. ADDERLEY, R., BOND, J., (2007), Predicting Crime Scene Attendance. Policing; An International Journal of Police Strategies & Management 7(42) pp 292-305.

15. ADDERLEY, R., BADII, A., WU, C., (2008), The Automatic Identification and Prioritisation of Criminal Networks from Police Crime Data Proceedings EuroISI 2008, European Conference on Intelligence and Security Informatics London, Springer-Verlag, pp 5-14.

16. T. Winkler, T. Kostoulas, R. Adderley, C. Bonkowski, T. Ganchev, J. Köhler, N. Fakotakis, (2008). "The MoveOn Motorcycle Speech Corpus", in Proc. of the LREC'2008, Marrakech, Morocco.

17. Adderley, Richard; Smith, Michelle (2010). "Position Paper: Assessing Stress in Operational Police Officers". IPES/DCAF Working Paper Series, No 22 (July).

18. Adderley, R., Smith, M. (2010). Position paper: Assessing stress in immersive training environments. 6th Nordic Conference on human-computer interaction, workshop. Reykjavik, Iceland. 16 – 20 October 2010.

19. Adderley, R., Badii, A., Chojnaicki, S. (2010). Investigating distraction burglary offences by the use of text mining. Submitted to: The International Journal of Police Science and Management.

20. M. Bäumer, P. Seidler, R. Torkar, P. Tomaszewski, L.-O. Damm and R. Feldt. "Predicting fault inflow in highly iterative software development processes: An industrial evaluation," In Proceedings of the 19th International Symposium on Software Reliability Engineering (ISSRE 2008), Seattle, USA, 2008.