A computation-oriented multimedia data streams model for content-based information retrieval

Chang, Shi-Kuo; Zhao, Lei; Guirguis, Shenoda; Kulkarni, Rohit

doi:10.1007/s11042-009-0372-y

Cited by 7 publications

(6 citation statements)

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“…Although multimedia databases have been in use for over 20 years, the legacy model for storage—housing static multimedia objects in a database—cannot support the processing necessary for high performance multimedia data streams. In order to effectively process dynamic multimedia data streams and continuous queries, multimedia data stream management systems (MMDSMS) have recently been introduced in the context of a computation‐oriented multimedia data stream model [7]. This model is based on a generalization of the data stream model to allow for multimedia data streams.…”

Section: Related Workmentioning

confidence: 99%

“…The MMDSMS concept also involves content‐based information retrieval. Although the authors of [7] introduce several basic features of a system that processes multimedia data streams, unfortunately, they did not provide a solution to the problem of processing of audio samples and audio streams. Moreover, it is our understanding that none of the existing MMDSMSs supports audio stream processing, which is one of the most important multimedia tasks.…”

Section: Related Workmentioning

confidence: 99%

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Similarity based join over audio feeds in a multimedia data stream management system

Maison¹,

Majda²,

Dobrowolski³

et al. 2013

Bell Labs Tech. J.

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Over the last several years, processing of high performance data streams has become very important in various domains. A new type of data processing is needed for applications where input data streams are modeled as multimedia data streams, such as audio and video feeds. For example, in the public safety sector, monitoring and automatic identification of particular individuals suspected of terrorist or criminal activity requires the processing of complex audio and video streams, which is beyond the capabilities of a typical data stream management system (DSMS). The concept of a multimedia data stream management system (MMDSMS) has recently been introduced in order to effectively process continuous queries over dynamic multimedia data streams. In this paper, we address MMDSMS functionalities related to speaker recognition problems in the area of detecting individuals who may pose security threats. We focus on audio feed processing using our novel similarity‐based join and on parameterization of the multimedia signal for the process of recognition. We propose a set of signal parameters which a clearly discriminate among individual voices by describing the signal using a homomorphic processing method. Our research was primarily focused on assessing the applicability of cepstral analysis in speech recognition systems, based on a set of acquired digitized voice samples. We developed a research prototype to assess the proposed concepts, and verified the effectiveness of our framework in a lab environment. © 2013 Alcatel‐Lucent.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Similarity based join over audio feeds in a multimedia data stream management system

Maison¹,

Majda²,

Dobrowolski³

et al. 2013

Bell Labs Tech. J.

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“…There are several commercial implementations of multimedia support in conventional DBMSs; however, these DBMSs are not adequate for data stream queries, because operations are evaluated on static snapshots of data stored in relations. In order to effectively process dynamic multimedia data streams, continuous queries and multimedia data stream management systems (MMDSMS) have been recently introduced in a computation-oriented multimedia data stream model [9]. We have extended STREAM to support multimedia data and query processing including use of a novel similarity-based join.…”

Section: Multimedia Support In Cqlmentioning

confidence: 99%

Monitoring high performance data streams in vertical markets: Theory and applications in public safety and healthcare

Maison¹,

Steen²,

Zakrzewicz³

et al. 2011

Bell Labs Tech. J.

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“…Multimedia dependencies and constraints for the multiple (quick/slow) decision cycles can also be integrated into the ontological knowledge base. In our approach we developed a Multimedia Data Streams model (MMDSM) to provide a formal framework to achieve efficient content based retrieval [6]. We also extended the Multimedia Functional Dependency Theory and the Normalization Framework to handle Multimedia Data Streams [7].…”

Section: Query Evolution (Adaptation)mentioning

confidence: 99%

Editorial: A General Framework for Slow Intelligence Systems

Chang

2010

Int. J. Soft. Eng. Knowl. Eng.

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Contrary to popular belief, not all intelligent systems have Quick Intelligence. There are a surprisingly large number of intelligent systems, quasi-intelligent systems and semiintelligent systems that have Slow Intelligence. Such Slow Intelligence Systems are often neglected in mainstream research on intelligent systems, but they are really worthy of our attention and emulation. I will describe the characteristics of Slow Intelligence Systems and present a general framework for Slow Intelligence Systems. I will then discuss evolutionary query processing and mission control in emergency management systems as two examples of Artificial Slow Intelligence Systems. Researchers and practitioners are both invited to explore the applications of Slow Intelligence Systems in software engineering and knowledge engineering, and publish their findings in IJSEKE.

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A computation-oriented multimedia data streams model for content-based information retrieval

Cited by 7 publications

References 10 publications

Similarity based join over audio feeds in a multimedia data stream management system

Similarity based join over audio feeds in a multimedia data stream management system

Monitoring high performance data streams in vertical markets: Theory and applications in public safety and healthcare

Editorial: A General Framework for Slow Intelligence Systems

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