Real-time Burst Detection Research Articles

Real-time Spread Burst Detection in Data Streaming

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Approach for Near-Real-Time Pipe Burst Detection and Location Estimation Utilizing Any Sequence of Harmonics of the Transient Pressure Signal

The purpose of the paper is to present an approach for real-time pipe burst detection and location estimation, based on the changes in the harmonics and analysis of the corresponding damping that is caused by the burst. The amplitude of each resonance response of the transient pressure wave caused by the burst is damped differently due to the occurrence of the burst. However, utilizing higher order harmonics beyond the first few fundamental frequencies has not previously been possible as it leads to an increasing number of possible solutions to the burst location, which limits the application of the method. An algorithm for damping analysis has been developed which overcomes this limitation, and is able to exclude most of the incorrect solutions when utilizing the information contained in higher order harmonics. Additionally, the gap between data windows can be set so that it enables the real-time data analysis by letting the window gap equal the reciprocal of the sampling rate. Therefore, this algorithm makes utilizing any sequence of harmonics of the signal be possible for real-time burst detection and location estimation. The approach has been verified both numerically and experimentally with acceptable accuracy.

Real-time burst detection based on multiple features of pressure data

Abstract Pipe bursts are an essential issue for water loss in water distribution systems. This study proposes a real-time burst detection method that combines multiple data features of multiple time steps. The method sets burst thresholds in three dimensions according to different moments at a specific monitoring point, and achieves burst identification based on a classification model. First, three data features, namely, absolute pressure value, predicted deviation value obtained by pressure variation value, of historical pressure at each time step are scored based on the Western Electric Company rules. The scores represent different abnormalities. Then, the scores corresponding to the three features are used as input of the decision tree classification model. The trained model is used for detecting burst events. Results show that this method achieves 99.56% detection accuracy, indicating that it is effective for burst detection. The proposed method outperformed the single-feature-based method and provides good results in water distribution systems.

Real-time detection of bursts in neuronal cultures using a neuromorphic auditory sensor and spiking neural networks

The correct identification of burst events is crucial in many scenarios, ranging from basic neuroscience to biomedical applications. However, none of the burst detection methods that can be found in the literature have been widely adopted for this task. As an alternative to conventional techniques, a novel neuromorphic approach for real-time burst detection is proposed and tested on acquisitions from in vitro cultures. The system consists of a Neuromorphic Auditory Sensor, which converts the input signal obtained from electrophysiological recordings into spikes and decomposes them into different frequency bands. The output of the sensor is sent to a trained Spiking Neural Network implemented on a SpiNNaker board that discerns between bursting and non-bursting activity. This data-driven approach was compared with different conventional spike-based and raw-based burst detection methods, addressing some of their drawbacks, such as being able to detect both high and low frequency events and working in an online manner. Similar results in terms of number of detected events, mean burst duration and correlation as current state-of-the-art approaches were obtained with the proposed system, also benefiting from its lower power consumption and computational latency. Therefore, our neuromorphic-based burst detection paves the road to future implementations for real-time neuroprosthetic applications.

Real-Time Burst Detection in District Metering Areas in Water Distribution System Based on Patterns of Water Demand with Supervised Learning

This paper proposes a new method to detect bursts in District Metering Areas (DMAs) in water distribution systems. The methodology is divided into three steps. Firstly, Dynamic Time Warping was applied to study the similarity of daily water demand, extract different patterns of water demand, and remove abnormal patterns. In the second stage, according to different water demand patterns, a supervised learning algorithm was adopted for burst detection, which established a leakage identification model for each period of time, respectively, using a sliding time window. Finally, the detection process was performed by calculating the abnormal probability of flow during a certain period by the model and identifying whether a burst occurred according to the set threshold. The method was validated on a case study involving a DMA with engineered pipe-burst events. The results obtained demonstrate that the proposed method can effectively detect bursts, with a low false-alarm rate and high accuracy.

A Proposal of Highly Responsive Distributed Denial-of-Service Attacks Detection Using Real-Time Burst Detection Method

A Proposal of Highly Responsive Distributed Denial-of-Service Attacks Detection Using Real-Time Burst Detection Method

Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

Rapid detection of bursts and leaks in water distribution systems (WDSs) can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA) systems and the establishment of district meter areas (DMAs). Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.

A Nontriggered Burst Supplement to the BATSE Gamma‐Ray Burst Catalogs

The Burst and Transient Source Experiment (BATSE) on the Compton Gamma Ray Observatory detected gamma-ray bursts (GRBs) with a real-time burst detection (or "trigger") system running onboard the spacecraft. Under some circumstances, however, a GRB may not have activated the onboard burst trigger. For example, the burst may have been too faint to exceed the onboard detection threshold, or it may have occurred while the onboard burst trigger was disabled for technical reasons. This paper describes a catalog of 873 "non-triggered" GRBs that were detected in a search of the archival continuous data from BATSE, recorded between 1991 December 9.0 and 1997 December 17.0. For each burst, the catalog gives an estimated source direction, duration, peak flux, and fluence. Similar data are presented for 50 additional bursts of unknown origin that were detected in the 25--50 keV range; these events may represent the low-energy "tail" of the GRB spectral distribution. This catalog increases the number of GRBs detected with BATSE by 48% during the time period covered by the search.

The Intensity Distribution of Faint Gamma‐Ray Bursts Detected with BATSE

We have recently completed a search of six years of archival Burst and Transient Source Experiment (BATSE) data for gamma-ray bursts (GRBs) that were too faint to activate the real-time burst detection system running on board the spacecraft. These 'nontriggered' bursts can be combined with the 'triggered' bursts detected on board to produce a GRB intensity distribution that reaches peak fluxes a factor of approximately two lower than could be studied previously. The value of the statistic (in Euclidean space) for the bursts we detect is 0.177 +/- 0.006. This surprisingly low value is obtained because we detected very few bursts on the 4.096 s and 8.192 s timescales (where most bursts have their highest signal-to-noise ratio) that were not already detected on the 1.024 s timescale. If allowance is made for a power-law distribution of intrinsic peak luminosities, the extended peak flux distribution is consistent with models in which the redshift distribution of the gamma-ray burst rate approximately traces the star formation history of the universe. We argue that this class of models is preferred over those in which the burst rate is independent of redshift. We use the peak flux distribution to derive a limit of 10% (99% confidence) on the fraction of the total burst rate that could be contributed by a spatially homogeneous (in Euclidean space) subpopulation of burst sources, such as type Ib/c supernovae. These results lend support to the conclusions of previous studies predicting that relatively few faint 'classical' GRBs will be found below the BATSE onboard detection threshold.