Title
Modeling the behavior of large scale reasoning systems using clustering and regression
Abstract
Modeling the performance of large scale systems is the core idea of this paper.We focus on modeling the performance specific behavior of LarKC1- The Large Knowledge Collider a platform for large scale integrated reasoning and Web-search. A set of instrumentation and monitoring tools are employed to collect metrics related to execution time, resources, and specific platform measurements like running workflows and plug-ins. Our method performs machine learning on top of instrumented data and tries to find relations between input defined metrics and output metrics that describe the instrumentation observations of the LarKC platform, plug-ins or workflows. The proposed method is a combination of clustering and regression techniques.
Year
DOI
Venue
2011
10.1109/ICCP.2011.6047863
ICCP
Keywords
Field
DocType
internet,inference mechanisms,information retrieval,knowledge management,pattern clustering,regression analysis,larkc,web-search,clustering technique,large knowledge collider,large scale integrated reasoning system,machine learning,plug-ins,regression technique,prediction model,predictive models,cognition,mathematical model,measurement,computational modeling,computer model
Data mining,Regression,Numerical models,Regression analysis,Pattern clustering,Computer science,Artificial intelligence,Execution time,Cluster analysis,Workflow,Machine learning,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4577-1481-8
0
0.34
References 
Authors
9
5
Name
Order
Citations
PageRank
Raluca Brehar162.15
Ion Giosan222.06
Andrei Vatavu3375.47
Mihai Negru4224.80
Sergiu Nedevschi51321126.37