DocumentCode
573339
Title
Adaptive Employee Profile Classification for Resource Planning Tool
Author
Gonzalez, Tere ; Santos, Pano ; Orozco, Fernando ; Alcaraz, Mildreth ; Zaldivar, Victor ; Obeso, A.D. ; Garcia, Alan
Author_Institution
Inf. Analytics Lab., HP Labs., Palo Alto, CA, USA
fYear
2012
fDate
24-27 July 2012
Firstpage
544
Lastpage
553
Abstract
Matching the right people to the right job considering constraints such as qualifications, availability and cost is the cornerstone of IT projects delivery services. We present a study to improve data accuracy and completeness for resource matching by integrating unstructured data sources and introducing text mining techniques to dynamically adapt resource profile for resource planning decisions. Our approach discovers resource categories by extracting and learning new patterns from employee resumes; and incorporating resource experience for the job-matching optimization during the resource planning exercise.
Keywords
data mining; optimisation; personnel; recruitment; text analysis; IT projects delivery services; adaptive employee profile classification; data accuracy; job-matching optimization; resource planning tool; text mining techniques; unstructured data sources; Dictionaries; Ontologies; Planning; Resumes; Text mining; Resource planning; bag of words; information extraction; naïve Bayes classifier; nlp; nltk; ontology; optimization; relation discovery; supervised classification; text mining;
fLanguage
English
Publisher
ieee
Conference_Titel
SRII Global Conference (SRII), 2012 Annual
Conference_Location
San Jose, CA
ISSN
2166-0778
Print_ISBN
978-1-4673-2318-5
Electronic_ISBN
2166-0778
Type
conf
DOI
10.1109/SRII.2012.67
Filename
6311037
Link To Document