Finding Periodic Outliers over a Monogenetic Event Stream

2005 ◽  
Author(s):  
K. Kuramitsu
Keyword(s):  
Event Stream

scholarly journals Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks

2021 ◽  
pp. 1-1
Author(s):  
Yanxiang Wang ◽  
Xian Zhang ◽  
Yiran Shen ◽  
Bowen Du ◽  
Guangrong Zhao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Event Stream

VMV-GCN: Volumetric Multi-View Based Graph CNN for Event Stream Classification

2022 ◽  
pp. 1-1
Author(s):  
Bochen Xie ◽  
Yongjian Deng ◽  
Zhanpeng Shao ◽  
Hai Liu ◽  
You-Fu Li
Keyword(s):  
Stream Classification ◽  
Event Stream

High-frequency river chemistry unveils the inner workings of concentration-discharge relationships during flood events

2021 ◽  
Author(s):  
Paul Floury ◽  
Julien Bouchez ◽  
Jérôme Gaillardet ◽  
Arnaud Blanchouin ◽  
Patrick Ansart
Keyword(s):  
High Frequency ◽  
Agricultural Watershed ◽  
Hydrological Processes ◽  
Rain Event ◽  
Exact Nature ◽  
Flood Events ◽  
Event Stream ◽  
Major Species ◽  
Solute Fluxes ◽  
Short Time

<p>Shifts in water fluxes through the Critical Zone exert a major control on stream solute export, but the exact nature of this control is still obscure, especially at the scale of relatively short flood events. To address this question, here we take advantage of a new high-frequency, flood event stream concentration–discharge (C-Q) dataset. Stream dissolved concentration of major species were recorded every 40 minutes over five major flood events in 2015/2016 recorded in a French agricultural watershed using device called the "River Lab". We focus our attention on the flood recession periods to highlight how C-Q relationships are controlled by hydrological processes within the catchment rather than by the dynamics of the rain event.</p><p>We show that for C-Q relationships resulting from data acquisition over multi-year time scales and including several flood events, lumping all trends together potentially result in biases in characteristic parameters (such as exponents of a power-law fit), that are strongly dictated by data from the recession periods of the most intense floods alone.</p><p>In order to evaluate the role of mixing of pre-existing water and solute pools in the catchment, we apply to solute fluxes an approach previously developed in catchment hydrology linking water storage and stream flow. This approach, which considers that hydrological processes prevail over chemical interactions during the short time spans of flood events, allows us to reproduce at first order a large diversity of shapes of recession C-Q relationships.</p>

Event Stream

2009 ◽  
pp. 1063-1063
Author(s):  
Opher Etzion
Keyword(s):  
Event Stream

Event Stream

2018 ◽  
pp. 1393-1394
Author(s):  
Opher Etzion
Keyword(s):  
Event Stream

scholarly journals Prefix Imputation of Orphan Events in Event Stream Processing

2021 ◽  
Vol 4 ◽  
Author(s):  
Rashid Zaman ◽  
Marwan Hassani ◽  
Boudewijn F. Van Dongen
Keyword(s):  
Process Model ◽  
Process Mining ◽  
State Of The Art ◽  
Processing System ◽  
Conformance Checking ◽  
Event Stream ◽  
Window Width ◽  
Event Logs ◽  
Relevant Case ◽  
Log File

In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is conformant with an existing process model. This inspection is additionally quantifying the conformance in an explainable manner. Online conformance checking processes streaming event logs by having precise insights into the running cases and timely mitigating non-conformance, if any. State-of-the-art online conformance checking approaches bound the memory by either delimiting storage of the events per case or limiting the number of cases to a specific window width. The former technique still requires unbounded memory as the number of cases to store is unlimited, while the latter technique forgets running, not yet concluded, cases to conform to the limited window width. Consequently, the processing system may later encounter events that represent some intermediate activity as per the process model and for which the relevant case has been forgotten, to be referred to as orphan events. The naïve approach to cope with an orphan event is to either neglect its relevant case for conformance checking or treat it as an altogether new case. However, this might result in misleading process insights, for instance, overestimated non-conformance. In order to bound memory yet effectively incorporate the orphan events into processing, we propose an imputation of missing-prefix approach for such orphan events. Our approach utilizes the existing process model for imputing the missing prefix. Furthermore, we leverage the case storage management to increase the accuracy of the prefix prediction. We propose a systematic forgetting mechanism that distinguishes and forgets the cases that can be reliably regenerated as prefix upon receipt of their future orphan event. We evaluate the efficacy of our proposed approach through multiple experiments with synthetic and three real event logs while simulating a streaming setting. Our approach achieves considerably higher realistic conformance statistics than the state of the art while requiring the same storage.

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