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Methodol."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    <jats:italic toggle=\"yes\">Context<\/jats:italic>\n                    : Anomaly detection is crucial for maintaining cloud-based software systems, as it enables early identification and resolution of unexpected failures. Given rapid and significant advances in the anomaly detection domain and the complexity of its industrial implementation, an overview of techniques that utilize real-world operational data is needed.\n                    <jats:italic toggle=\"yes\">Aim<\/jats:italic>\n                    : This study aims to complement existing research with an extensive catalog of the techniques and monitoring data used for detecting anomalies affecting the performance or reliability of cloud-based software systems that have been developed and\/or evaluated in a real-world context.\n                    <jats:italic toggle=\"yes\">Method<\/jats:italic>\n                    : We perform a systematic mapping study to examine the literature on anomaly detection in cloud-based systems, particularly focusing on the usage of real-world monitoring data, with the aim of identifying key data categories, tools, data preprocessing, and anomaly detection techniques.\n                    <jats:italic toggle=\"yes\">Results<\/jats:italic>\n                    : Based on a review of 104 papers, we categorize monitoring data by structure, types, and origins and the tools used for data collection and processing. We offer a comprehensive overview of data preprocessing and anomaly detection techniques mapped to different data categories. Our findings highlight practical challenges and considerations in applying these techniques in real-world cloud environments.\n                    <jats:italic toggle=\"yes\">Conclusion<\/jats:italic>\n                    : The findings help practitioners and researchers identify relevant data categories and select appropriate data preprocessing and anomaly detection techniques for their specific operational environments, which is important for improving the reliability and performance of cloud-based systems.\n                  <\/jats:p>","DOI":"10.1145\/3744556","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T09:09:34Z","timestamp":1750151374000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Monitoring Data for Anomaly Detection in Cloud-Based Systems: A Systematic Mapping Study"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4575-1460","authenticated-orcid":false,"given":"Adha","family":"Hrusto","sequence":"first","affiliation":[{"name":"Department of Computer Science, Lund University, Lund, Sweden and System Verification, Malm\u00f6, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7266-5632","authenticated-orcid":false,"given":"Nauman bin","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Blekinge Institute of Technology, Karlskrona, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6736-9425","authenticated-orcid":false,"given":"Emelie","family":"Engstr\u00f6m","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Lund University, Lund, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0175-005X","authenticated-orcid":false,"given":"Yuqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"issue":"9","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1016\/j.comnet.2013.04.001","article-title":"Cloud monitoring: A survey","volume":"57","author":"Aceto Giuseppe","year":"2013","unstructured":"Giuseppe Aceto, Alessio Botta, Walter De Donato, and Antonio Pescap\u00e8. 2013. 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