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Performance-preserving event log sampling for predictive monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such...
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Automated generation of initial points for adaptive rejection sampling of log-concave distributions
Adaptive rejection sampling requires that users provide points that span the distribution’s mode. If these points are far from the mode, it...
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Complexity of zigzag sampling algorithm for strongly log-concave distributions
We study the computational complexity of zigzag sampling algorithm for strongly log-concave distributions. The zigzag process has the advantage of...
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Adaptive Sampling for Weighted Log-Rank Survival Trees Boosting
The field of survival analysis is devoted to predicting the probability and time of the occurrence of an event. The global problem is to predict the...
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Event Log Sampling for Predictive Monitoring
Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such...
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Exact Distributed Sampling
Fast distributed algorithms that output a feasible solution for constraint satisfaction problems, such as maximal independent sets, have been heavily...
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Dimension-independent spectral gap of polar slice sampling
Polar slice sampling, a Markov chain construction for approximate sampling, performs, under suitable assumptions on the target and initial...
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Maximum likelihood estimation of log-concave densities on tree space
Phylogenetic trees are key data objects in biology, and the method of phylogenetic reconstruction has been highly developed. The space of...
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Sampling Algorithms
Nearly all of the data structures and algorithms we reviewed in the previous chapters are designed specifically for either nearest neighbor search or...
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Analyzing and predicting job failures from HPC system log
In this paper, we analyze the scheduler log of a production supercomputer that contains complete job information, which is in contrast to many...
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Fast Geometric Sampling for Phong-Like Reflection
Importance sampling is a critical technique for reducing the variance of Monte Carlo samples. However, the classical importance sampling based on the...
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Log Anomaly Detection Based on Semantic Features and Topic Features
System logs serve as crucial data sources for monitoring system performance and enhancing service quality. Many existing log-based anomaly detection...
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Towards Learning the Optimal Sampling Strategy for Suffix Prediction in Predictive Monitoring
Predictive monitoring is a subfield of process mining which focuses on forecasting the evolution of an ongoing process case. A related main challenge...
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Evidential uncertainty sampling strategies for active learning
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and...
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Detecting log anomaly using subword attention encoder and probabilistic feature selection
Log anomaly is a manifestation of a software system error or security threat. Detecting such unusual behaviours across logs in real-time is the...
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Markov chain importance sampling for minibatches
This study investigates importance sampling under the scheme of minibatch stochastic gradient descent, under which the contributions are twofold....
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An adaptive graph sampling framework for graph analytics
In large-scale data processing, graph analytics of complex interaction networks are indispensable. As the whole graph processing and analytics can be...
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HEART: Heterogeneous Log Anomaly Detection Using Robust Transformers
Log sequences generated by heterogeneous systems are critical for understanding computer system behaviour and ensuring operational and security...