Reactive business intelligence

From Wikipedia, the free encyclopedia

Reactive business intelligence (RBI) advocates an holistic approach that integrates data mining, modeling and interactive visualization, into an end-to-end discovery and continuous innovation process powered by human and automated learning.[1]

In the area of decision-making this approach has been used to adapt the decision method to the knowledge which is progressively acquired from the decision maker.[2]

Relationships with reactive search optimization (RSO)

RSO is multi-disciplinary

RSO is a multi-disciplinary research area between operations research (optimization), computer science, machine learning and neural networks that studies online learning schemes applied to problem-solving and optimization, according to a learning while optimizing principle .[3] The word reactive hints at a ready response to events during the search through an internal feedback loop for online self-tuning and dynamic adaptation. Online adaptation and model revision is used in reactive business intelligence to adapt the data mining and interactive visualization techniques to the knowledge derived from a user in real time.

References

  1. ↑ Roberto Battiti and Mauro Brunato, Reactive Business Intelligence. From Data to Models to Insight, Reactive Search Srl, Italy, February 2011. ISBN 978-88-905795-0-9.
  2. ↑ Battiti, Roberto; Andrea Passerini (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker." (PDF). IEEE Transactions on Evolutionary Computation 14 (15): 671–687. doi:10.1109/TEVC.2010.2058118 
  3. ↑ Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Reactive Search and Intelligent Optimization. Springer Verlag. ISBN 978-0-387-09623-0. 

See also

This article is issued from Wikipedia. The text is available under the Creative Commons Attribution/Share Alike; additional terms may apply for the media files.