A Versatile Event-Driven Data Model in HBase Database for Multi-source Data of Power Grid

Liu, Bin; Zhu, Yongxin; Chang, Wang; Chen, Yufeng; Huang, Tian; Shi, Weiwei; Li, Mengjun; Mao, Yishu

doi:10.1109/smartcloud.2016.28

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Over the past years, the researchers proposed several methods to store different datasets on HBase in different ways [14][15][16][17]. Most of the research was published worldwide in journals and conferences.…”

Section: Contributionsmentioning

confidence: 99%

A Comprehensive Study of HBase Storage Architecture—A Systematic Literature Review

et al. 2021

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According to research, generally, 2.5 quintillion bytes of data are produced every day. About 90% of the world’s data has been produced in the last two years alone. The amount of data is increasing immensely. There is a fight to use and store this tremendous information effectively. HBase is the top option for storing huge data. HBase has been selected for several purposes, including its scalability, efficiency, strong consistency support, and the capacity to support a broad range of data models. This paper seeks to define, taxonomically classify, and systematically compare existing research on a broad range of storage technologies, methods, and data models based on HBase storage architecture’s symmetry. We perform a systematic literature review on a number of published works proposed for HBase storage architecture. This research synthesis results in a knowledge base that helps understand which big data storage method is an effective one.

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Section: Contributionsmentioning

confidence: 99%

A Comprehensive Study of HBase Storage Architecture—A Systematic Literature Review

et al. 2021

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“…Only in this way, can HBase be used successfully for CPS time series analytics under a scenario of multiple data sources. Several works have been proposed to accommodate the time series from different data sources [17]. The purpose of the proposed model in [9] is to cleanse data from different data sources and then transform data into a unified data format such as JSON or XML .…”

Section: Storage Model For Dependable Time Series Analytics On Hbasementioning

confidence: 99%

“…Our proposed DTSA framework is designed and implemented on the basis of a proposed eventdriven storage model [17], HBase platform and Redundant Arrays of Independent Disks (RAID). So in this section, we briefly introduce the storage structure of HBase, the event-driven storage model proposed by previous researchers and the basics of RAID system.…”

Section: Prerequisitesmentioning

confidence: 99%

“…In our proposed DTSA framework, we use a buffering system based on RAID10 and an extraction engine to transforming raw SG time series into a format suited for the unified event-driven storage model [17].…”

Section: Event-driven Storage Model For Sg Datamentioning

confidence: 99%

“…The target database in our proposed DTSA framework is HBase on top of a Hadoop platform. To extract the time series, we use a proposed storage model: event-driven storage model [17]. The preliminary refining method is to recreate the rowkey of the raw time series from the CPS based Figure 4.…”

Section: Dependable Data Extractionmentioning

confidence: 99%

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A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid

Wang

Zhu

Shi

et al. 2018

ACM Trans. Cyber-Phys. Syst.

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With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. IoT (Internet of Things) based Smart Grid (SG) is one of the foundations of this CPS revolution and defined as a power grid integrated with a large network of smart objects. The volume of time series of SG equipment is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing a tremendous volume of raw time series thereby providing a solid support for precise time series analytics now becomes tricky. In this paper, we propose a dependable time series analytics (DTSA) framework for IoT-based SG. Our proposed DTSA framework is capable of proving a dependable data transforming from CPS to the target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-networkregularization based matrix factorization (SnrMF) method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online lightweight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.

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An Interface for User-Centred Process and Correlation Between Large Datasets

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Vassilakis

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Design, User Experience, and Usability: UX Research and Design

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A Versatile Event-Driven Data Model in HBase Database for Multi-source Data of Power Grid

Cited by 7 publications

References 14 publications

A Comprehensive Study of HBase Storage Architecture—A Systematic Literature Review

A Comprehensive Study of HBase Storage Architecture—A Systematic Literature Review

A Dependable Time Series Analytic Framework for Cyber-Physical Systems of IoT-based Smart Grid

An Interface for User-Centred Process and Correlation Between Large Datasets

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