@article{SchneiderWenigPapenbrock2021,
  author    = {Schneider, Johannes and Wenig, Phillip and Papenbrock, Thorsten},
  title     = {Distributed detection of sequential anomalies in univariate time series},
  series = {The VLDB journal : the international journal on very large data bases},
  volume    = {30},
  journal   = {The VLDB journal : the international journal on very large data bases},
  number    = {4},
  publisher = {Springer},
  address   = {Berlin},
  issn      = {1066-8888},
  doi       = {10.1007/s00778-021-00657-6},
  pages     = {579 -- 602},
  year      = {2021},
  abstract  = {The automated detection of sequential anomalies in time series is an essential task for many applications, such as the monitoring of technical systems, fraud detection in high-frequency trading, or the early detection of disease symptoms. All these applications require the detection to find all sequential anomalies possibly fast on potentially very large time series. In other words, the detection needs to be effective, efficient and scalable w.r.t. the input size. Series2Graph is an effective solution based on graph embeddings that are robust against re-occurring anomalies and can discover sequential anomalies of arbitrary length and works without training data. Yet, Series2Graph is no t scalable due to its single-threaded approach; it cannot, in particular, process arbitrarily large sequences due to the memory constraints of a single machine. In this paper, we propose our distributed anomaly detection system, short DADS, which is an efficient and scalable adaptation of Series2Graph. Based on the actor programming model, DADS distributes the input time sequence, intermediate state and the computation to all processors of a cluster in a way that minimizes communication costs and synchronization barriers. Our evaluation shows that DADS is orders of magnitude faster than S2G, scales almost linearly with the number of processors in the cluster and can process much larger input sequences due to its scale-out property.},
  language  = {en}
}
@article{WenigSchmidlPapenbrock2022,
  author    = {Wenig, Phillip and Schmidl, Sebastian and Papenbrock, Thorsten},
  title     = {TimeEval: a benchmarking toolkit for time series anomaly detection algorithms},
  series = {Proceedings of the VLDB Endowment},
  volume    = {15},
  journal   = {Proceedings of the VLDB Endowment},
  number    = {12},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY},
  issn      = {2150-8097},
  doi       = {10.14778/3554821.3554873},
  pages     = {3678 -- 3681},
  year      = {2022},
  abstract  = {Detecting anomalous subsequences in time series is an important task in time series analytics because it serves the identification of special events, such as production faults, delivery bottlenecks, system defects, or heart flicker. Consequently, many algorithms have been developed for the automatic detection of such anomalous patterns. The enormous number of approaches (i.e., more than 158 as of today), the lack of properly labeled test data, and the complexity of time series anomaly benchmarking have, though, led to a situation where choosing the best detection technique for a given anomaly detection task is a difficult challenge. In this demonstration, we present TIMEEVAL, an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms. TIMEEVAL includes an extensive data generator and supports both interactive and batch evaluation scenarios. With our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.},
  language  = {en}
}