Using Control Theory to Guide Load Shedding in Medical Data Stream Management System

2005 ◽  
pp. 236-248 ◽  
Author(s):  
Zijing Hu ◽  
Hongyan Li ◽  
Baojun Qiu ◽  
Lv-an Tang ◽  
Yu Fan ◽  
...  
Keyword(s):  
Control Theory ◽  
Management System ◽  
Data Stream ◽  
Medical Data ◽  
Load Shedding ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management System

scholarly journals Content-based load shedding in multimedia data stream management system

2012 ◽  
Vol 37 (2) ◽  
pp. 79-95
Author(s):  
Rafal Maison ◽  
Maciej Zakrzewicz
Keyword(s):  
Data Streams ◽  
Management System ◽  
Data Stream ◽  
Public Safety ◽  
Load Shedding ◽  
Multimedia Data ◽  
Data Stream Management ◽  
Stream Management ◽  
Overload Management ◽  
Data Stream Management System

Abstract.Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding techniques should be introduced into the infrastructure to monitor and optimize the execution of multimedia data stream queries. In this paper, we present a novel content-centered load shedding framework, based on searching and matching algorithms, for analysing video tuples arriving within multimedia data streams. The framework tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We have extended our Continuous Query Language (CQL) syntax to enable this load shedding technique. The effectiveness of the framework has been verified using both artificial and real data video streams collected from monitoring devices.

STREAM: The Stanford Data Stream Management System

2016 ◽  
pp. 317-336 ◽  
Author(s):  
Arvind Arasu ◽  
Brian Babcock ◽  
Shivnath Babu ◽  
John Cieslewicz ◽  
Mayur Datar ◽  
...  
Keyword(s):  
Management System ◽  
Data Stream ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management System
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