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Efficient Tracing and Versatile Analysis of Lock Contention in Java Applications on the Virtual Machine Level.

, , , and . ICPE, page 263-274. ACM, (2016)

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Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?, , , and . ICPE Companion, page 9-12. ACM, (2019)An Approach for Ranking Feature-based Clustering Methods and its Application in Multi-System Infrastructure Monitoring., , , , , and . SEAA, page 178-187. IEEE, (2021)Selecting Time Series Clustering Methods based on Run-Time Costs., , and . Softwaretechnik-Trends, 40 (3): 49-51 (2020)Using Crash Frequency Analysis to Identify Error-Prone Software Technologies in Multi-System Monitoring., , , and . QRS, page 183-190. IEEE, (2018)Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study., , , and . Proc. ACM Hum. Comput. Interact., 4 (EICS): 75:1-75:37 (2020)Using Multi-System Monitoring Time Series to Predict Performance Events., , , , and . Softwaretechnik-Trends, 39 (3): 55-57 (2019)A Framework for Preprocessing Multivariate, Topology-Aware Time Series and Event Data in a Multi-System Environment., , , , and . HASE, page 115-122. IEEE, (2019)On the Difficulties of Supervised Event Prediction based on Unbalanced Real-World Data in Multi-System Monitoring., , , , and . Softwaretechnik-Trends, 39 (4): 38-40 (2019)Efficient Tracing and Versatile Analysis of Lock Contention in Java Applications on the Virtual Machine Level., , , and . ICPE, page 263-274. ACM, (2016)Efficient Sampling-based Lock Contention Profiling for Java., , , and . ICPE, page 331-334. ACM, (2017)