Leveraging AI in Service Automation Modeling: From Classical AI Through Deep Learning to Combination Models

Autor:	Larisa Shwartz, Genady Ya. Grabarnik, Jinho Hwang, Michael Nidd, Qing Wang
Rok vydání:	2019
Předmět:	Service automation business.industry Computer science Deep learning Enabling Key (cryptography) Cloud computing Artificial intelligence Resolution (logic) Service provider business Data science Automation
Zdroj:	Service-Oriented Computing ISBN: 9783030337018 ICSOC
DOI:	10.1007/978-3-030-33702-5_14
Popis:	With the advent of cloud, new generations of digital services are being conceived to respond to the ever-growing demands and expectations of the market place. In parallel, automations are becoming an essential enabler for successful management of these services. With such importance being placed on digital services, automated management of these services – in particular, automated incident resolution – becomes a key issue for both the provider and the users. The challenge facing automation providers lies in variability and the frequently changing nature of the monitoring tickets that provide the primary input to automation. Despite the value of the correct automation at the correct time, it is also important to remember that triggering an incorrect automation may damage the smooth operation of the business. In this paper, we discuss AI modeling for automation recommendations. We describe a wide range of experiments which allowed us to conclude an optimal method with respect to accuracy and speed acceptable to service providers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9738c867e984f2f2048fd3afe8a36872 https://doi.org/10.1007/978-3-030-33702-5_14 Zobrazit plný text záznamu