• 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