Cloud based architecture for enabling intuitive decision making

Proceedings - 2013 IEEE 9th World Congress on Services, SERVICES 2013

Volumn , Issue , 2013, Pages 60-66

  Xu, Brian ^a   Kumar, Sathish Alampalayam ^b   Kumar, Manonmani ^c  

^a HONEYWELL   (United States)

^b INFOSYS TECHNOLOGIES LTD   (India)

^c UTSW Medical Center   (United States)

Author keywords

Intuitive Decision Engine; Genetic Algorithms; Cloud Computing; Brain Computer Interface; Measures of Intuition

Indexed keywords

AUTOMATED MACHINES; CLOUD-BASED ARCHITECTURES; DECISION ENGINES; FALSE NEGATIVE RATE; IMPLEMENTED ARCHITECTURES; INNOVATIVE TECHNOLOGY; MAKING TECHNOLOGY; PROCESSING CAPABILITY;

DATA PROCESSING; DECISION MAKING; ENGINES; GENETIC ALGORITHMS; LEARNING SYSTEMS; TECHNOLOGY;

BRAIN COMPUTER INTERFACE;

EID: 84888048800     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SERVICES.2013.11     Document Type: Conference Paper

References (17)

1. Szalma, James L and Peter A. Hancock, Task Loading and Stress in Human-Computer Interaction: Theoretical Frameworks and Mitigation Strategies Handbook for Human-Computer Interaction in Interactive Systems. (pp. 115-132), (2nd Edition). Mahwah, N.J: Erlbaum, 2008
2. Hodgkinson, Gerard P., Janice Langan-Fox, and Eugene Sadler-Smith, Intuition: A Fundamental Bridging Construct in the Behavioral Sciences British Journal of Psychology. 99, no. 1:1-27, 2008
3. I. Ari, E. Olmezogullari and F. Celebi, Data stream analytics and mining in the cloud IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom) pp. 857-862, 2012
4. S. Chaisiri et al., Workflow framework to support data analytics in cloud computing IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 610-613, 2012
5. L. Zhang, Big Services Era: Global Trends of Cloud Computing and Big Data IEEE Transactions on Services Computing, pp. 467-468, 2012
6. S. Guo et al., A MapReduce-based System for Time-Based Big Data Analytics IEEE International Conference on Cluster Computing (CLUSTER), pp. 72-80, 2012
7. N. Leavitt, Bringing Big Analytics to the Masses Computer, pp. 20-23, 2013
8. Brian Xu, Andrew Kostrzewski, Networked Intelligent Agents for Distributed Decision Making The Intl Conf. on Artificial Intelligence (ICAI), Las Vegas, 2007
9. Brian Xu, Quarterly, Blackboard Pattern-Enabled Integrated Intelligent Toolset The U.S.A. Army, I2WD, CERDEC, Fort Monmouth, NJ, 2008
10. Hadoop http://hadoop.apache.org/ Accessed February 2, 2013
11. Cloud Map Reduce http://code.google.com/p/cloudmapreduce/ Accessed February, 2013
12. S4 http://incubator.apache.org/s4/ Accessed February, 2013
13. Mahout http://mahout.apache.org/ Accessed February, 2013
14. Emotiv EPOC http://en.wikipedia.org/wiki/Emotiv/Emotiv EPOC Accessed Feb 2013
15. Crowley et al., Evaluating a Brain-Computer Interface to Categorise Human Emotional Response IEEE International Conference on Advanced Learning Technologies, pp. 276-278, 2010
16. Neurosky www.neurosky.com Accessed February, 2013
17. OpenEEG http://openeeg.sourceforge.net/doc/ Accessed February, 2013