DocumentCode :
2264831
Title :
A learning feature engineering method for task assignment
Author :
Loewenstern, David ; Pinel, Florian ; Shwartz, Larisa ; Gatti, Maíra ; Herrmann, Ricardo ; Cavalcante, Victor
Author_Institution :
IBM TJ Watson Res. Center, Hawthorne, NY, USA
fYear :
2012
fDate :
16-20 April 2012
Firstpage :
961
Lastpage :
967
Abstract :
Multi-domain IT services are delivered by technicians with a variety of expert knowledge in different areas. Their skills and availability are an important property of the service. However, most organizations do not have a consistent view of this information because creation and maintenance of a skill model is a difficult task, especially in light of privacy regulations, changing service catalogs and worker turnover. We propose a method for ranking technicians on their expected performance according to their suitability for receiving the assignment of a service request without maintaining an explicit skill model describing which skills are possessed by each technician. We find appropriate assignees by making use of similarities between the assignees and previous tasks performed by them.
Keywords :
expert systems; learning (artificial intelligence); personnel; expert knowledge; learning feature engineering method; multidomain IT services; privacy regulations; service catalogs; task assignment; technician ranking method; worker turnover; Dispatching; Hidden Markov models; Organizations; Servers; Support vector machines; Training data; Vectors; SVM; adaptive features; feature engineering; machine learning; request fulfillment; service management; task assignment; ticket dispatching;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Operations and Management Symposium (NOMS), 2012 IEEE
Conference_Location :
Maui, HI
ISSN :
1542-1201
Print_ISBN :
978-1-4673-0267-8
Electronic_ISBN :
1542-1201
Type :
conf
DOI :
10.1109/NOMS.2012.6212015
Filename :
6212015
Link To Document :
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