<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Content-based load shedding in multimedia data stream management system

Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60- 965 Poznan, Poland

Foundations of Computing and Decision Sciences

{"article-title":"Content-based load shedding in multimedia data stream management system"}

Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of...

Content-based load shedding in multimedia data stream management system

Pubblicato online: 01 ott 2012

Pagine: 79 - 95

DOI: https://doi.org/10.2478/v10209-011-0007-8

Parole chiave
data streams, load shedding, data stream management system, overload management.

This content is open access.

Content-based load shedding in multimedia data stream management system

Pubblicato online: 01 ott 2012

Pagine: 79 - 95

DOI: https://doi.org/10.2478/v10209-011-0007-8

Parole chiavedata streams, load shedding, data stream management system, overload management.

This content is open access.

Parole chiave
data streams, load shedding, data stream management system, overload management.