A Web 2.0‐based science gateway for massive remote sensing image processing

Miao, Yanhua; Wang, Lizhe; Ma, Yan; Zhang, Wanfeng; Chen, Lajiao

doi:10.1002/cpe.3049

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Frame difference analysis is mainly used to detect adjacent frames of the image to capture the movement. The adjacent two frames will be compared, and a live showing room is suspected illegal when the computed difference is larger than a tuned threshold [21,22,23,24]. The threshold can be determined and set by an experienced administrator of platforms, and it should be changed dynamically to approach the better false positives and false negatives [25].…”

Section: Frame Difference Analysis Methodsmentioning

confidence: 99%

RIMS: A Real-time and Intelligent Monitoring System for live-broadcasting platforms

Ren

Zhu

et al. 2018

Future Generation Computer Systems

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Personal live shows on Internet streaming platform currently is blooming as a popular application and attracting several millions of users. The content supervision of live streaming platform, in which hundreds of show rooms perform synchronously, is a major concern with the development of this new kind of service. Traditional image captures and real-time content analysis experience huge difficulties, such as processing delay, data overwhelming and analysis overhead. In this paper, we propose several novel methods and algorithms to monitor real-time live stream and identify illegal live misbehavior intelligently based on state of perception and frame difference analysis. The proposed system makes use of several indicators of the chatting room status rather than analyzing images solely and directly. Three kinds of detecting techniques are adopted: self-adaptive threshold-based abnormal traffic detection, sensitive comments perception, and state evaluation. The proposed system captures the segmentation of video scenes by frame difference analysis, and pays more attention on the showing room that has large scene changes. We deploy our system to monitor a typical live platform called panda.tv, and the overall accuracy of three indicators reaches 90.1%. The application of this system can change current situation where the live platform supervision totally depends on manual review. The key techniques in this system can be widely employed in many applications, such as live broadcast platform industries, video surveillance for state security, and the national security for counter-terrorism.

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Section: Frame Difference Analysis Methodsmentioning

confidence: 99%

RIMS: A Real-time and Intelligent Monitoring System for live-broadcasting platforms

Ren

Zhu

et al. 2018

Future Generation Computer Systems

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“…Recently, several researchers have begun to see Cloud computing technology as a cost-effective and highly scalable solution to Big Data problems in different 35 domains such as geospatial sciences and threat management [10]. Cloud computing provides elastic and on-demand access to an almost infinite amount of storage, network and computational resources [11].…”

Section: Introductionmentioning

confidence: 99%

Cloud computing based bushfire prediction for cyber–physical emergency applications

Garg

Aryal

Wang

et al. 2018

Future Generation Computer Systems

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In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users' needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users' requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements.

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“…This special issue also investigates the cloud‐based image processing space in science, and it does this in the context of the Science Gateway for Massive Remote Sensing Image Processing (SGMRSIP) that is used to processes/analyze satellite images. More specifically, the paper titled ‘A Web 2.0‐based Science Gateway for Massive Remote Sensing Image Processing’ introduces a Web 2.0‐based browser/server model that is designed for SGMRSIP. This incorporates a cloud‐based image processing portal that is built on the top of a high performance computing cluster, and a client/server interaction model.…”

mentioning

confidence: 99%

A note on resource orchestration for cloud computing

Ranjan

Buyya

Nepal

et al. 2014

Concurrency and Computation

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Welcome to the special issue of Concurrency and Computation: Practice and Experience (CCPE) journal. This special issue compiles a number of excellent technical contributions that significantly advance the state-of-the-art in the areas of orchestrating cloud resources, composing new cloud services from existing ones, increasing energy efficiency via cloud resource orchestration, and developing cloud-based image processing solutions.Over the past few years, cloud computing [1][2][3][4] has emerged as the latest and most dominant utility computing solution offering both hardware and software resources as virtualization-enabled services. Cloud computing providers such as Amazon Web Services and Microsoft Azure currently provide application owners the option of deploying their applications over a network of a virtually infinite resource pool with practically no up-front capital investment and with operating cost proportional to the actual use (i.e., implementing a pay-as-you-go model). An increasing number of cloud vendors offer information and communication technology (ICT) resources such as hardware (CPUs, GPUs, storage, and networks), software infrastructure (e.g., databases, webservers, stream-processing systems, and data-mining packages), and collaboration/communication applications (e.g., email, video on demand, and social networks) as infrastructure as a service (IAAS), platform as a service (PAAS), and software as a service (SAAS), respectively. This approach allows enterprises to easily, cost effectively, and reliably offer business services that are supported by computing and software resources that are provided and maintained by IAAS, PAAS, and SAAS providers. This makes cloud computing attractive to especially small and medium size enterprises (SMEs), as it allows them to focus more on their core business and less on ICT infrastructure.One of the fundamental issues in exploiting cloud computing in this fashion is developing better Resource Orchestration (RO) [1][2][3][4][5] techniques and programming frameworks. More specifically, Resource Orchestration (RO) is 'the set of operations that cloud providers (e.g., AWS) and application owners (e.g., Netflix) undertake (either manually or automatically via computer programs) for selecting, deploying, monitoring, and dynamically controlling configuration of hardware and software resources as a system of QoS assured components that can be seamlessly delivered to end-users' [1]. Since RO operations span across all layers of cloud computing stack [1], an overall goal of RO is to ensure successful hosting and delivery of applications (SAAS) by managing the fulfillment of the QoS objectives of both the application owners (e.g., maximize availability, maximize throughput, minimize latency, and avoid overloading) and the Cloud resource providers (e.g., maximize utilization, maximize energy efficiency, and maximize profit).One of the main complexities in Cloud resource management is that Cloud resources are typically identified by unique functional specifications, and...

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A Web 2.0‐based science gateway for massive remote sensing image processing

Cited by 11 publications

References 21 publications

RIMS: A Real-time and Intelligent Monitoring System for live-broadcasting platforms

RIMS: A Real-time and Intelligent Monitoring System for live-broadcasting platforms

Cloud computing based bushfire prediction for cyber–physical emergency applications

A note on resource orchestration for cloud computing

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