A Parallelized Data Stream Processing System Using Dynamic Time Warping Distance

2009 ◽  
Author(s):  
Norihiro Takahashi ◽  
Tomoki Yoshihisa ◽  
Yasushi Sakurai ◽  
Masanori Kanazawa
Keyword(s):  
Dynamic Time Warping ◽  
Data Stream ◽  
Stream Processing ◽  
Processing System ◽  
Time Warping ◽  
Data Stream Processing ◽  
Dynamic Time

An In-Camera Data Stream Processing System for Defect Detection in Web Inspection Tasks

1999 ◽  
Vol 5 (1) ◽  
pp. 23-34 ◽  
Author(s):  
S.Hossain Hajimowlana ◽  
Roberto Muscedere ◽  
Graham A. Jullien ◽  
James W. Roberts
Keyword(s):  
Defect Detection ◽  
Data Stream ◽  
Stream Processing ◽  
Processing System ◽  
Data Stream Processing ◽  
Web Inspection ◽  
Inspection Tasks

scholarly journals QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4160
Author(s):  
Isam Mashhour Al Jawarneh ◽  
Paolo Bellavista ◽  
Antonio Corradi ◽  
Luca Foschini ◽  
Rebecca Montanari
Keyword(s):  
Data Streams ◽  
Spatial Data ◽  
Data Stream ◽  
Smart Cities ◽  
Stream Processing ◽  
Processing System ◽  
Strategic Decision ◽  
Approximate Query Processing ◽  
Data Stream Processing ◽  
Mobility Data

Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

RFID data stream processing technology based on CEP

2009 ◽  
Vol 29 (10) ◽  
pp. 2786-2790 ◽  
Author(s):  
Xiao-jia YIN ◽  
Shi-guang JU ◽  
Ying-jie WANG
Keyword(s):  
Data Stream ◽  
Stream Processing ◽  
Processing Technology ◽  
Data Stream Processing ◽  
Rfid Data
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