Optimal Employee Recruitment in Organizations under Attribute-Based Access Control.

Employee Assignment Optimization Graph Coloring Greedy algorithm H.1.2 [Models and Principles]: User/Machine Systems Role Based Access Control (RBAC) Separation of Duty Statically Mutually Exclusive Roles (SMER) constraint

Journal

ACM transactions on management information systems
ISSN: 2158-656X
Titre abrégé: ACM Trans Manag Inf Syst
Pays: United States
ID NLM: 101638918

Informations de publication

Date de publication:
Jan 2021
Historique:
entrez: 30 4 2021
pubmed: 1 5 2021
medline: 1 5 2021
Statut: ppublish

Résumé

For any successful business endeavor, recruitment of required number of appropriately qualified employees in proper positions is a key requirement. For effective utilization of human resources, reorganization of such workforce assignment is also a task of utmost importance. This includes situations when the under-performing employees have to be substituted with fresh applicants. Generally, the number of candidates applying for a position is large and hence, the task of identifying an optimal subset becomes critical. Moreover, a human resource manager would also like to make use of the opportunity of retirement of employees to improve manpower utilization. However, the constraints enforced by the security policies prohibit any arbitrary assignment of tasks to employees. Further, the new employees should have the capabilities required to handle the assigned tasks. In this article, we formalize this problem as the Optimal Recruitment Problem (ORP), wherein the goal is to select the minimum number of fresh employees from a set of candidates to fill the vacant positions created by the outgoing employees, while ensuring satisfiability of the specified security conditions. The model used for specification of authorization policies and constraints is Attribute Based Access Control (ABAC), since it is considered to be the

Identifiants

pubmed: 33927914
doi: 10.1145/3403950
pmc: PMC8078840
mid: NIHMS1647775
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM118574
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM134927
Pays : United States

Références

Eur J Popul. 1998 Mar;14(1):39-59
pubmed: 12293880

Auteurs

Arindam Roy (A)

Goa Institute of Management, India.

Shamik Sural (S)

Indian Institute of Technology, Kharagpur, India.

Arun Kumar Majumdar (AK)

Indian Institute of Technology, Kharagpur, India.

Jaideep Vaidya (J)

Rutgers University, USA.

Vijayalakshmi Atluri (V)

Rutgers University, USA.

Classifications MeSH