scholarly journals Towards an advanced system for real-time event detection in high-volume data streams

2012 ◽  
Author(s):  
Andreas Weiler ◽  
Svetlana Mansmann ◽  
Marc H. Scholl
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
High Volume ◽  
Volume Data ◽  
Advanced System

A comparison of select trigger algorithms for automated global seismic phase and event detection

1998 ◽  
Vol 88 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Mitchell Withers ◽  
Richard Aster ◽  
Christopher Young ◽  
Judy Beiriger ◽  
Mark Harris ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Spectral Characteristics ◽  
Digital Data ◽  
Robust Detection ◽  
Location System ◽  
Global Correlation ◽  
Seismic Coda ◽  
Wide Range

Abstract Digital algorithms for robust detection of phase arrivals in the presence of stationary and nonstationary noise have a long history in seismology and have been exploited primarily to reduce the amount of data recorded by data logging systems to manageable levels. In the present era of inexpensive digital storage, however, such algorithms are increasingly being used to flag signal segments in continuously recorded digital data streams for subsequent processing by automatic and/or expert interpretation systems. In the course of our development of an automated, near-real-time, waveform correlation event-detection and location system (WCEDS), we have surveyed the abilities of such algorithms to enhance seismic phase arrivals in teleseismic data streams. Specifically, we have considered envelopes generated by energy transient (STA/LTA), Z-statistic, frequency transient, and polarization algorithms. The WCEDS system requires a set of input data streams that have a smooth, low-amplitude response to background noise and seismic coda and that contain peaks at times corresponding to phase arrivals. The algorithm used to generate these input streams from raw seismograms must perform well under a wide range of source, path, receiver, and noise scenarios. Present computational capabilities allow the application of considerably more robust algorithms than have been historically used in real time. However, highly complex calculations can still be computationally prohibitive for current workstations when the number of data streams become large. While no algorithm was clearly optimal under all source, receiver, path, and noise conditions tested, an STA/LTA algorithm incorporating adaptive window lengths controlled by nonstationary seismogram spectral characteristics was found to provide an output that best met the requirements of a global correlation-based event-detection and location system.

REAL-TIME HIGH-VOLUME DATA TRANSFER AND PROCESSING FOR e-VLBI

2010 ◽  
pp. 81-90
Author(s):  
YASUHIRO KOYAMA ◽  
TETSURO KONDO ◽  
MORITAKA KIMURA ◽  
MASAKI HIRABARU ◽  
HIROSHI TAKEUCHI
Keyword(s):  
Real Time ◽  
Data Transfer ◽  
High Volume ◽  
Volume Data

Scalable distance-based outlier detection over high-volume data streams

2014 ◽  
Author(s):  
Lei Cao ◽  
Di Yang ◽  
Qingyang Wang ◽  
Yanwei Yu ◽  
Jiayuan Wang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
High Volume ◽  
Volume Data

Visual Analysis of Dynamic Data Streams

2009 ◽  
Vol 8 (3) ◽  
pp. 212-229 ◽  
Author(s):  
George Chin ◽  
Mudita Singhal ◽  
Grant Nakamura ◽  
Vidhya Gurumoorthi ◽  
Natalie Freeman-Cadoret
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Volume ◽  
Scientific Data ◽  
General Strategy ◽  
Dynamic Visualization ◽  
Time Data ◽  
Dynamic Data ◽  
Intensive Training ◽  
Real Time Data

For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension of high-volume, real-time data. The general strategy of the project is to develop and evolve visual contexts that will organize and orient high-volume dynamic data in conceptual and perceptive views. The goal is to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. Thus far, the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. We describe the design considerations and unique features of these dynamic visualization prototypes as well as our findings in the exploration and evaluation of their use.

Real-Time Event Detection for Energy Data Streams

2014 ◽  
pp. 221-225 ◽  
Author(s):  
Aqeel H. Kazmi ◽  
Michael J. O’Grady ◽  
Gregory M. P. O’Hare
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Event Detection ◽  
Energy Data

Online Analysis of High-Volume Data Streams in Astroparticle Physics

2015 ◽  
pp. 100-115 ◽  
Author(s):  
Christian Bockermann ◽  
Kai Brügge ◽  
Jens Buss ◽  
Alexey Egorov ◽  
Katharina Morik ◽  
...  
Keyword(s):  
Data Streams ◽  
High Volume ◽  
Volume Data ◽  
Astroparticle Physics ◽  
Online Analysis

scholarly journals A Parameter Space Framework for Online Outlier Detection Over High-Volume Data Streams

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 38124-38136 ◽  
Author(s):  
Guanzhe Zhao ◽  
Yanwei Yu ◽  
Peng Song ◽  
Geng Zhao ◽  
Zhe Ji
Keyword(s):  
Outlier Detection ◽  
Parameter Space ◽  
Data Streams ◽  
High Volume ◽  
Volume Data
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