Paper Info
Title
Estimating Fungibility Between Skills by Combining Skill-Similarities Obtained from Multiple Data Sources
Abstract
This paper proposes an approach to estimating fungibility between skills given multiple information sources of those skills. An estimate of skill adjacency or fungibility or substitutability is critical for effective capacity planning, analytics and optimization in the face of changing skill requirements of an organization. The proposed approach is based on computing a similarity measure between skills, using each available data source, and combining these similarities into a measure of fungibility. We present both supervised and unsupervised integration methods and demonstrate that these produce improved outcomes, compared to using any single skill similarity source alone, using data from a large IT organization. The skills' fungibility matrix created using this approach has been deployed by the organization for clustering skills for use in demand forecasting. A possible extension of this work is to use the fungibility measure to cluster skills and develop a skill-centric representation of an organization to enable strategic assessments and planning.
Year
DOI
Venue
2017
10.1109/ICDMW.2017.36
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Keywords
Field
DocType
Skill-similarity,Data integration,Fungibility,Demand forecasting,Skill-capacity analytics
Data integration,Fungibility,Demand forecasting,Similarity measure,Computer science,Capacity planning,Artificial intelligence,Analytics,Cluster analysis,Semantics,Machine learning
Conference
ISSN
ISBN
Citations 
2375-9232
978-1-5386-3801-9
0
PageRank 
References 
Authors
0.34
7
7
Name
Order
Citations
PageRank
Vasudevan, S.1132.11
Moninder Singh2381105.12
Joydeep Mondal321.77
Michael Peran452.17
Ben Zweig500.34
Brian Johnston620.73
Rachel Rosenfeld720.73