Towards Real-Time Analytics in the Cloud

Osman, Amr; El-Refaey, Mohamed; Elnaggar, A.

doi:10.1109/services.2013.36

Cited by 29 publications

(14 citation statements)

References 36 publications

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“…With the real-time data which is from the cloud storage platform [14], the data of all the SCADA can be displayed in the plant monitoring center and published on the web. Using Component Art UI and High chart, different ways of display are realized, such as GIS-Simulation, pie, line chart; display of the real-time data in all region and the history data analysis of oil/water wells [15].…”

Section: Real-time Monitoringmentioning

confidence: 99%

Construction of Cloud Storage Platform based on the MongoDB and its Application in the Oilfield Production

Yang¹,

Huang²,

Zhao³

et al. 2018

dtcse

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With the rapid development of the digital constructions in Huabei oilfield, the area that the automatic monitoring systems must cover becomes larger and larger over times. In addition, the systems built in different local region face many problems, such as the un-unified data format, decentral storage, difficult to share, and low data utilization. Based on MongoDB, a cloud storage platform is built, aiming at synchronizing all the real-time production data from the different automatic monitoring systems to the unified storage format. Then, the secondary development of unified production data on this platform can easily realize the oil/water well on-line analysis, 3D-simulation, real-time monitoring, report, etc. The built cloud platform will satisfy the demand of the integration and optimization for production engineering.

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Section: Real-time Monitoringmentioning

confidence: 99%

Construction of Cloud Storage Platform based on the MongoDB and its Application in the Oilfield Production

Yang¹,

Huang²,

Zhao³

et al. 2018

dtcse

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“…Millions of reading per second would be reported at substation levels, which should be compared against variety of data like consumer profile, historical data, weather data and predictions made in time Many distributed stream computing platforms are available for effective real time computation on big data streams [8] -like Yahoo S4 [15], Apache Storm [16], Apache Spark [9]. Spark introduces in memory partitions and computing, thereby reducing frequent hard disk reads and writes, which improves the response time which is the key characteristic of stream computing.…”

Section: A Streaming Data Analyticsmentioning

confidence: 99%

A Survey on Realtime Analytics Framework for Smart Grid Energy Management

K.Sornalakshmi¹

2015

IJIRSET

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Smart grids are modernized electricity grids with information technology support. Smart Grids are the most promising development in the energy and utilities market.Smart grids are being installed in many countries and it is expected to have multi-fold benefits in efficient energy management. The Smart Grids receive real time meter data with high velocity and volume. In such scenario, near real time efficient analytics of streaming smart meter data and quick decision making is significant. In this paper, we survey the existing methodologies and means for real time energy data management in smart grids.

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“…Finally, other less specific works focus on reviewing the state-of-the-art of real-time platforms for distributed analytics [52] and looking at other research challenges and issues to develop a platform for analyzing big data on real time [53].…”

Section: State Of the Artmentioning

confidence: 99%

A scalable machine learning online service for big data real-time analysis

Baldominos

Albacete

Sáez

et al. 2014

2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD)

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Abstract-This work describes a proposal for developing and testing a scalable machine learning architecture able to provide real-time predictions or analytics as a service over domain-independent big data, working on top of the Hadoop ecosystem and providing real-time analytics as a service through a RESTful API. Systems implementing this architecture could provide companies with on-demand tools facilitating the tasks of storing, analyzing, understanding and reacting to their data, either in batch or stream fashion; and could turn into a valuable asset for improving the business performance and be a key market differentiator in this fast pace environment. In order to validate the proposed architecture, two systems are developed, each one providing classical machine-learning services in different domains: the first one involves a recommender system for web advertising, while the second consists in a prediction system which learns from gamers' behavior and tries to predict future events such as purchases or churning. An evaluation is carried out on these systems, and results show how both services are able to provide fast responses even when a number of concurrent requests are made, and in the particular case of the second system, results clearly prove that computed predictions significantly outperform those obtained if random guess was used.

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Towards Real-Time Analytics in the Cloud

Cited by 29 publications

References 36 publications

Construction of Cloud Storage Platform based on the MongoDB and its Application in the Oilfield Production

Construction of Cloud Storage Platform based on the MongoDB and its Application in the Oilfield Production

A Survey on Realtime Analytics Framework for Smart Grid Energy Management

A scalable machine learning online service for big data real-time analysis

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