TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Alam, Aftab; Lee, Young-Koo

doi:10.3390/s20123581

“…Fig. 3 VBDCL is the foundation layer and is responsible for large-scale big data management throughout the life-cycle of IVA, i.e., from data acquisition to early persistence to archival and deletion [48]. VBDPL is responsible for distributed video preprocessing, feature extraction, etc.…”

Section: Lambda Cvas: a Reference Architecturementioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

Alam

¹

,

Ullah

²

,

Lee

³

2020

Self Cite

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The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

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“…Orchestration and Optimization of IVA Pipeline: The real-time and batch workflow are deeply dependent on the messaging middleware and distributed processing engines. The dynamic (R/B)IVA service creation and multisubscription environment demand the optimization and orchestration of the IVA service pipeline [3] while guarantees opportunities for further research. In the map-reduce infrastructure, a slowdown predictor can be utilized to improve the agility and timeliness of scheduling decisions.…”

Section: Research Issues Opportunities and Future Directionsmentioning

confidence: 99%

Video Big Data Analytics in the Cloud: Research Issues and Challenges

Alam¹,

Shah²,

Khan³

et al. 2020

Preprint

Self Cite

0

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On the rise of distributed computing technologies, video big data analytics in the cloud have attracted researchers and practitioners' attention. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide an architecture on video big data analytics in the cloud, including managing and analyzing video big data, the challenges, and opportunities. This study proposes a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. This is the first study that presents the generalized view of the video big data analytics in the cloud to the best of our knowledge. Keywords: big data • intelligent video analytics • cloud-based video analytics system • video analytics review • deep learning • distributed computing • intermediate results orchestration • cloud computing.

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“…Fig. VBDCL is the foundation layer and is responsible for large-scale big data management throughout the life-cycle of IVA, i.e., from data acquisition to early persistence to archival and deletion [48]. VBDPL is responsible for distributed video pre-processing, feature extraction, etc.…”

Section: Lambda Cvas: a Reference Architecturementioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

Alam,

Ullah,

Lee

2020

Preprint

Self Cite

0

View full text Add to dashboard Cite

The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

show abstract

TORNADO: Intermediate Results Orchestration Based Service-Oriented Data Curation Framework for Intelligent Video Big Data Analytics in the Cloud

Cited by 7 publications

References 32 publications

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

Video Big Data Analytics in the Cloud: Research Issues and Challenges

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

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