Classification-Based Barrier Change Point Detection Methods Artemii Patov, Viacheslav Gorikhovskii, Vladimir Kutuev Conference of Open Innovation Association Fruct, 2025 The change point detection problem in the time series arises in a wide variety of fields. In some situations, we lack the necessary resources to apply complex techniques, so lightweight approaches are needed.In this study, we consider lightweight approaches to the change point detection problem, namely, classification-based barrier methods. We want to investigate the use of various classifiers and classification evaluation metrics for change point detection, so we are creating a framework that makes it easy to build methods from different components. To study a large number of constructed methods, we create a flexible benchmarking system that allows one to evaluate methods using different metrics.We conduct an empirical study of the methods, present the results and compare them with existing methods and with each other. Our implementation of the KNN based method shows high-quality results. However, we see potential in using at least one more tested classifier as well.
Evaluation of the context-free path querying algorithm based on matrix multiplication Nikita Mishin, Iaroslav Sokolov, Egor Spirin, Vladimir Kutuev, Egor Nemchinov, Sergey Gorbatyuk, Semyon Grigorev Proceedings of the ACM SIGACT SIGMOD SIGART Symposium on Principles of Database Systems, 2019 Recently proposed matrix multiplication based algorithm for context-free path querying (CFPQ) offloads the most performance-critical parts onto boolean matrices multiplication. Thus, it is possible to achieve high performance of CFPQ by means of modern parallel hardware and software. In this paper, we provide results of empirical performance comparison of different implementations of this algorithm on both real-world data and synthetic data for the worst cases.
