Service Level Agreements (SLAs) are contracts co-signed by an Application Service Provider (ASP) and the end user(s) to regulate the services delivered through the Internet. They contain several clauses establishing for example the level of the services to guarantee, also known as quality of service (QoS) parameters and the penalties to apply in case the requirements are not met during the SLA validity time. SLAs use legal jargon, indeed they have legal validity in case of court litigation between the parties. A dedicated contract management facility should be part of the service provisioning because of the contractual importance and contents. Some work in literature about these facilities rely on a structured language representation of SLAs in order to make them machine-readable. The majority of these languages are the result of private stipulation and not available for public services where SLAs are expressed in common natural language instead. In order to automate the SLAs management, in this paper we present an investigation towards SLAs text recognition. We devised an approach to identify the definitions and the constraints included in the SLAs using different machine learning techniques and provide a preliminary assessment of the approach on a set of 36 publicly available SLA documents.

Towards automatic Service Level Agreements information extraction

DE MARCO, LUCIA;FERRUCCI, Filomena;NAPOLI, GENNARO;SALZA, PASQUALE
2016-01-01

Abstract

Service Level Agreements (SLAs) are contracts co-signed by an Application Service Provider (ASP) and the end user(s) to regulate the services delivered through the Internet. They contain several clauses establishing for example the level of the services to guarantee, also known as quality of service (QoS) parameters and the penalties to apply in case the requirements are not met during the SLA validity time. SLAs use legal jargon, indeed they have legal validity in case of court litigation between the parties. A dedicated contract management facility should be part of the service provisioning because of the contractual importance and contents. Some work in literature about these facilities rely on a structured language representation of SLAs in order to make them machine-readable. The majority of these languages are the result of private stipulation and not available for public services where SLAs are expressed in common natural language instead. In order to automate the SLAs management, in this paper we present an investigation towards SLAs text recognition. We devised an approach to identify the definitions and the constraints included in the SLAs using different machine learning techniques and provide a preliminary assessment of the approach on a set of 36 publicly available SLA documents.
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9789897581823
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4672707
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