16,488 research outputs found
Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma
A novel algorithm and implementation of real-time identification and tracking
of blob-filaments in fusion reactor data is presented. Similar spatio-temporal
features are important in many other applications, for example, ignition
kernels in combustion and tumor cells in a medical image. This work presents an
approach for extracting these features by dividing the overall task into three
steps: local identification of feature cells, grouping feature cells into
extended feature, and tracking movement of feature through overlapping in
space. Through our extensive work in parallelization, we demonstrate that this
approach can effectively make use of a large number of compute nodes to detect
and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion
simulation data, we observed linear speedup on 1024 processes and completed
blob detection in less than three milliseconds using Edison, a Cray XC30 system
at NERSC.Comment: 14 pages, 40 figure
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Stacking-based visualization of trajectory attribute data
Visualizing trajectory attribute data is challenging because it involves showing the trajectories in their spatio-temporal context as well as the attribute values associated with the individual points of trajectories. Previous work on trajectory visualization addresses selected aspects of this problem, but not all of them. We present a novel approach to visualizing trajectory attribute data. Our solution covers space, time, and attribute values. Based on an analysis of relevant visualization tasks, we designed the visualization solution around the principle of stacking trajectory bands. The core of our approach is a hybrid 2D/3D display. A 2D map serves as a reference for the spatial context, and the trajectories are visualized as stacked 3D trajectory bands along which attribute values are encoded by color. Time is integrated through appropriate ordering of bands and through a dynamic query mechanism that feeds temporally aggregated information to a circular time display. An additional 2D time graph shows temporal information in full detail by stacking 2D trajectory bands. Our solution is equipped with analytical and interactive mechanisms for selecting and ordering of trajectories, and adjusting the color mapping, as well as coordinated highlighting and dedicated 3D navigation. We demonstrate the usefulness of our novel visualization by three examples related to radiation surveillance, traffic analysis, and maritime navigation. User feedback obtained in a small experiment indicates that our hybrid 2D/3D solution can be operated quite well
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
On the Nature and Types of Anomalies: A Review
Anomalies are occurrences in a dataset that are in some way unusual and do
not fit the general patterns. The concept of the anomaly is generally
ill-defined and perceived as vague and domain-dependent. Moreover, despite some
250 years of publications on the topic, no comprehensive and concrete overviews
of the different types of anomalies have hitherto been published. By means of
an extensive literature review this study therefore offers the first
theoretically principled and domain-independent typology of data anomalies, and
presents a full overview of anomaly types and subtypes. To concretely define
the concept of the anomaly and its different manifestations, the typology
employs five dimensions: data type, cardinality of relationship, anomaly level,
data structure and data distribution. These fundamental and data-centric
dimensions naturally yield 3 broad groups, 9 basic types and 61 subtypes of
anomalies. The typology facilitates the evaluation of the functional
capabilities of anomaly detection algorithms, contributes to explainable data
science, and provides insights into relevant topics such as local versus global
anomalies.Comment: 38 pages (30 pages content), 10 figures, 3 tables. Preprint; review
comments will be appreciated. Improvements in version 2: Explicit mention of
fifth anomaly dimension; Added section on explainable anomaly detection;
Added section on variations on the anomaly concept; Various minor additions
and improvement
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