Among enterprise business processes, those related to HR management are characterized by conflicting issues: on one hand, the peculiarities of intellectual capital ask for rather expressive representation languages to convey as many facets as possible; on the other hand, such processes deal with huge amounts of resources to be managed. For handling HR management tasks, our approach combines the representation power of a logical language with the information processing efficiency of a DBMS. It has been implemented in a fully functioning platform, I.M.P.A.K.T., that we present here highlighting its peculiarities for three relevant business processes: skill matching, task/team composition and company core competence identification.

Embedding semantics in human resources management automation via SQL / Tinelli, Eufemia; Colucci, Simona; Donini, Francesco M.; DI SCIASCIO, Eugenio; Giannini, Silvia. - In: APPLIED INTELLIGENCE. - ISSN 0924-669X. - 46:4(2017), pp. 952-982. [10.1007/s10489-016-0868-x]

Embedding semantics in human resources management automation via SQL

TINELLI, Eufemia;COLUCCI, Simona;DI SCIASCIO, Eugenio;GIANNINI, Silvia
2017-01-01

Abstract

Among enterprise business processes, those related to HR management are characterized by conflicting issues: on one hand, the peculiarities of intellectual capital ask for rather expressive representation languages to convey as many facets as possible; on the other hand, such processes deal with huge amounts of resources to be managed. For handling HR management tasks, our approach combines the representation power of a logical language with the information processing efficiency of a DBMS. It has been implemented in a fully functioning platform, I.M.P.A.K.T., that we present here highlighting its peculiarities for three relevant business processes: skill matching, task/team composition and company core competence identification.
2017
Embedding semantics in human resources management automation via SQL / Tinelli, Eufemia; Colucci, Simona; Donini, Francesco M.; DI SCIASCIO, Eugenio; Giannini, Silvia. - In: APPLIED INTELLIGENCE. - ISSN 0924-669X. - 46:4(2017), pp. 952-982. [10.1007/s10489-016-0868-x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/103676
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