Service Hyperlink: Modeling and Reusing Partial Process Knowledge by Mining Event Dependencies among Sensor Data Services

Author(s):  
Meiling Zhu ◽  
Chen Liu ◽  
Jianwu Wang ◽  
Shen Su ◽  
Yanbo Han
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhongmei Zhang ◽  
Zhongguo Yang ◽  
Sikandar Ali ◽  
Muhammad Asshad ◽  
Shaher Suleman Slehat

With the fast development of Sensor Network, Internet of Things, mobile devices, and pervasive computing, enormous amounts of sensor devices are deployed in physical world. Data streams produced by these sensor devices, deployed broadly, can be used to create various value-added applications. Facing continuous, real-time, high-frequency, low-valued data streams, how to flexibly and efficiently cooperate them for creating valuable application is very crucial. In this study, we propose a service-oriented manner to realize flexible streams integration. It considers data stream produced by one sensor data as a stream data service and utilizes composing multiple services to realize the cooperation among sensor devices. Firstly, we propose a stream data service model based on Event-Condition-Action rules, which can encapsulate steam data as services and continuously and timely process stream data into value-added events. Then, we propose a declarative method which can dynamically compose stream data services. Based on two kinds of declarative rules, that is, sink-rules and connect-rules, multiple data streams can be dynamically integrated through flexible service composition. To ensure the performance of service composition, we also employ a sensor partition strategy and process multiple service compositions in parallel. Through comprehensive evaluations by experiments, our service composition method shows both good efficiency and effectiveness.


2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR


1996 ◽  
Vol 1 (1) ◽  
pp. 88-99
Author(s):  
Stephen A. Grzelak ◽  
Harrison Miles ◽  
Edward S. Szurkowski ◽  
William P. Weber

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