Anomaly Detection

The dataset contains performance values, Area Under the ROC Curve (AUC) and Average Precision (AP), of popular anomaly detection (AD) algorithms taken over a set of 9k AD benchmark datasets.

Datasets were initially published with the following paper:

Kandanaarachchi, S., Muñoz, M. A., Hyndman, R. J., & Smith-Miles, K. (2020). On normalization and algorithm selection for unsupervised outlier detection. Data Mining and Knowledge Discovery, 34(2), 309-354.

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For Internet-based service companies, anomaly detection on data streams is critical in troubleshooting, seeking to maintain service quality and reliability. Most of known detection methods have an underlying assumption that the data are always continuous. In practical applications, however, we learn that many real-world data are sporadic. It incurs particular challenges for the task of anomaly detection, for which the common preprocessing of downsampling on sporadic data can omit potential anomalies and delay alarms. 

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The dataset provided contains the invariants in the form of antecedent and consequent mined using Association Rule Mining with implicit measures such as Confidence, Support, Lift, etc. from an EPIC Plant (Electrical Power and Intelligent Control System).

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This work aims to identify anomalous patterns that could be associated with performance degradation and failures in datacenter nodes, such as Virtual Machines or Virtual Machines clusters. The early detection of anomalies can enable early remediation measures, such as Virtual Machines migration and resource reallocation before losses occur. One way to detect anomalous patterns in datacenter nodes is using monitoring data from the nodes, such as CPU and memory utilization.

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412 Views

Anomaly detection is a well-known topic in cybersecurity. Its application to the Internet of Things can lead to suitable protection techniques against problems such as denial of service attacks.

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1349 Views

Arbitrarily falling dices were photographed individually and monochromatically inside an Ulbricht sphere from two fixed perspectives. Overall, 11 dices with edge size 16 mm were used for 2133 falling experiments repeatedly. 5 of these dices were modified manually to have the following anomalies: drilled holes, missing dots, sawing gaps and scratches. All pictures in the uploaded pickle containers have a resolution of 400 times 400 pixels with normalized grey scale floating point values of 0 (black) through 1 (white).

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192 Views

Shoulder Physiotherapy Activity Recognition 9-Axis Dataset (SPARS9x) 

Suggested uses of this dataset include performing supervised classification analysis of physiotherapy exercises, or to perform out-of-distribution detection analysis with unlabeled activities of daily living data.
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868 Views

This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.

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271 Views

This data set comprises 4223 videos from a laser surface heat treatment process (also called laser heat treatment) applied to cylindrical workpieces made of steel. The purpose of the dataset is to detect anomalies in the laser heat treatment learning a model from a set of non-anomalous videos.

In the laser heat treatment, the laser beam is following a pattern similar to an "eight" with a frequency of 100 Hz. This pattern is sometimes modified to avoid obstacles in the workpieces.

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487 Views