TY - GEN
T1 - A Systematic Mapping Study in AIOps
AU - Notaro, Paolo
AU - Cardoso, Jorge
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and Big Data. However, past AIOps contributions are scattered, unorganized and missing a common terminology convention, which renders their discovery and comparison impractical. In this work, we conduct an in-depth mapping study to collect and organize the numerous scattered contributions to AIOps in a unique reference index. We create an AIOps taxonomy to build a foundation for future contributions and allow an efficient comparison of AIOps papers treating similar problems. We investigate temporal trends and classify AIOps contributions based on the choice of algorithms, data sources and the target components. Our results show a recent and growing interest towards AIOps, specifically to those contributions treating failure-related tasks (62%), such as anomaly detection and root cause analysis.
AB - IT systems of today are becoming larger and more complex, rendering their human supervision more difficult. Artificial Intelligence for IT Operations (AIOps) has been proposed to tackle modern IT administration challenges thanks to AI and Big Data. However, past AIOps contributions are scattered, unorganized and missing a common terminology convention, which renders their discovery and comparison impractical. In this work, we conduct an in-depth mapping study to collect and organize the numerous scattered contributions to AIOps in a unique reference index. We create an AIOps taxonomy to build a foundation for future contributions and allow an efficient comparison of AIOps papers treating similar problems. We investigate temporal trends and classify AIOps contributions based on the choice of algorithms, data sources and the target components. Our results show a recent and growing interest towards AIOps, specifically to those contributions treating failure-related tasks (62%), such as anomaly detection and root cause analysis.
KW - AIOps
KW - Artificial Intelligence
KW - Operations and Maintenance
UR - http://www.scopus.com/inward/record.url?scp=85111372838&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76352-7_15
DO - 10.1007/978-3-030-76352-7_15
M3 - Conference contribution
AN - SCOPUS:85111372838
SN - 9783030763510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 110
EP - 123
BT - Service-Oriented Computing – ICSOC 2020 Workshops - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Proceedings
A2 - Hacid, Hakim
A2 - Outay, Fatma
A2 - Paik, Hye-young
A2 - Alloum, Amira
A2 - Petrocchi, Marinella
A2 - Bouadjenek, Mohamed Reda
A2 - Beheshti, Amin
A2 - Liu, Xumin
A2 - Maaradji, Abderrahmane
PB - Springer Science and Business Media Deutschland GmbH
T2 - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
Y2 - 14 December 2020 through 17 December 2020
ER -