MAD: A Monitor System for Big Data Applications

Shi, M.R.; Yuan, Ruiping

doi:10.1007/978-3-319-23862-3_30

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…We also tried to look for proposals as regards the monitoring of the operation of Big Data ecosystems, or studies related to managing the response to incidents in this type of environment; however, we found only one paper in this sense, on a specific tool that focuses on performance and not on security [18], and another study carried out ten years ago on how to monitor distributed environments [19].…”

Section: Related Workmentioning

confidence: 99%

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Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

et al. 2020

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Big data ecosystems are increasingly important for the daily activities of any type of company. They are decisive elements in the organization, so any malfunction of this environment can have a great impact on the normal functioning of the company; security is therefore a crucial aspect of this type of ecosystem. When approaching security in big data as an issue, it must be considered not only during the creation and implementation of the big data ecosystem, but also throughout its entire lifecycle, including operation, and especially when managing and responding to incidents that occur. To this end, this paper proposes an incident response process supported by a private blockchain network that allows the recording of the different events and incidents that occur in the big data ecosystem. The use of blockchain enables the security of the stored data to be improved, increasing its immutability and traceability. In addition, the stored records can help manage incidents and anticipate them, thereby minimizing the costs of investigating their causes; that facilitates forensic readiness. This proposal integrates with previous research work, seeking to improve the security of big data by creating a process of secure analysis, design, and implementation, supported by a security reference architecture that serves as a guide in defining the different elements of this type of ecosystem. Moreover, this paper presents a case study in which the proposal is being implemented by using big data and blockchain technologies, such as Apache Spark or Hyperledger Fabric.

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Section: Related Workmentioning

confidence: 99%

“…Big data with blockchain [16,17] Big data operation monitor [18,19] Machine learning for security [20][21][22][23] Machine learning for incident response [24,25]…”

Section: Machine Learningmentioning

confidence: 99%

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

et al. 2020

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“…When mobile apps features are tested, these features can be tested with the help of codes. For instance, if the mobile app features are tested, the camera is tested 􀅫irst because of different features [46]. When features are tested, it is made sure to get the proper support of the operating system.…”

Section: S Development Apps and Quality Assurancementioning

confidence: 99%

Quality assurance for mobile applications

Basit¹,

Khan²,

Nisa³

2020

J. adv. tec. eng. res

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“…The Nagios-based abnormal resource alarm is raised under conditions of system resource overload, system performance deterioration or system outage. According to the alarm information,virtual instances would be adjusted and administrators could conduct system survey and analysis[48].…”

mentioning

confidence: 99%

A cloud-based remote sensing data production system

Yan

Wang

et al. 2018

Future Generation Computer Systems

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The data processing capability of existing remote sensing system has not kept pace with the amount of data typically received and need to be processed. Existing product services are not capable of providing users with a variety of remote sensing data sources for selection, either. Therefore, in this paper, we present a product generation programme using multisource remote sensing data, across distributed data centers in a cloud environment, so as to compensate for the low productive efficiency, less types and simple services of the existing system. The programme adopts "master-slave" architecture. Specifically, the master center is mainly responsible for the production order receiving and parsing, as well as task and data scheduling, results feedback, and so on; the slave centers are the distributed remote sensing data centers, which storage one or more types of remote sensing data, and mainly responsible for production task execution. In general, each production task only runs on one data center, and the data scheduling among centers adopts a "minimum data transferring" strategy. The logical workflow of each production task is organized based on knowledge base, and then turned into the actual executed workflow by Kepler. In addition, $ Fully documented templates are available in the elsarticle package on CTAN.

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MAD: A Monitor System for Big Data Applications

Cited by 5 publications

References 4 publications

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

Improving Incident Response in Big Data Ecosystems by Using Blockchain Technologies

Quality assurance for mobile applications

A cloud-based remote sensing data production system

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