Segmentation and dimensionality reduction

Bingham, Ella; Gionis, Aristides; Haiminen, Niina; Hiisilä, Heli; Mannila, Heikki; Terzi, Evimaria

doi:10.1137/1.9781611972764.33

Cited by 35 publications

(30 citation statements)

References 18 publications

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“…Basically, there are mainly three categories of time series segmentation algorithms using dynamic programming. Firstly, sliding windows [9] top-down [10], and bottom-up [11] strategies. The sliding windows method is a purely implicit segmentation technique.…”

Section: Foundationsmentioning

confidence: 99%

YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

Martí

Sánchez-Pi²,

Molina

et al. 2014

Lecture Notes in Computer Science

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Abstract. Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection. Offshore petroleum platforms rely on heavy turbomachines for its extraction, pumping and generation operations. Frequently, these machines are intensively monitored by hundreds of sensors each, which send measurements with a high frequency to a concentration hub. Handling these data calls for a holistic approach, as sensor data is frequently noisy, unreliable, inconsistent with a priori problem axioms, and of a massive amount. For the anomalies detection problems in turbomachinery, it is essential to segment the dataset available in order to automatically discover the operational regime of the machine in the recent past. In this paper we propose a novel time series segmentation algorithm adaptable to big data problems and that is capable of handling the high volume of data involved in problem contexts. As part of the paper we describe our proposal, analyzing its computational complexity. We also perform empirical studies comparing our algorithm with similar approaches when applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

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Section: Foundationsmentioning

confidence: 99%

YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

Martí

Sánchez-Pi²,

Molina

et al. 2014

Lecture Notes in Computer Science

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show abstract

“…Basically, there are mainly three categories of time series segmentation algorithms using dynamic programming. Firstly, sliding windows [17] top-down [18], and bottom-up [19] strategies. The sliding windows method is a purely implicit segmentation technique.…”

Section: Time Series Segmentationmentioning

confidence: 99%

Combining Support Vector Machines and Segmentation Algorithms for Efficient Anomaly Detection: A Petroleum Industry Application

Martí

Sánchez-Pi²,

Molina

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. Anomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct, or react to the situations associated with them. In that sense, heavy extraction machines for pumping and generation operations like turbomachines are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

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“…Some of them have provable approximation bounds [8,18], while others have proved to work very well in practice [5,11,14,13,17]. Variants of the basic SEGMEN-TATION problem have been studied in different contexts, for example [2,7,9]. The main idea in these cases is to find a segmentation of the input sequence into k segments, subject to some constraints imposed on the output representatives.…”

Section: Related Workmentioning

confidence: 99%

Segmentations with Rearrangements

Gionis¹,

Terzi²

2007

Proceedings of the 2007 SIAM International Conference on Data Mining

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Sequence segmentation is a central problem in the analysis of sequential and time-series data. In this paper we introduce and we study a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. Our problem formulation has applications in segmenting data collected from a sensor network where some of the sensors might be slightly out of sync, or in the analysis of newsfeed data where news reports on a few different topics are arriving in an interleaved manner. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even approximate. We then proceed to devise effective algorithms for the proposed problem, combining ideas from linear programming, dynamic programming, and outlier-detection algorithms in sequences. We perform extensive experimental evaluation on synthetic and real datasets that demonstrates the efficacy of the suggested algorithms.

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Segmentation and dimensionality reduction

Cited by 35 publications

References 18 publications

YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

YASA: Yet Another Time Series Segmentation Algorithm for Anomaly Detection in Big Data Problems

Combining Support Vector Machines and Segmentation Algorithms for Efficient Anomaly Detection: A Petroleum Industry Application

Segmentations with Rearrangements

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