A New Method for Time-Series Big Data Effective Storage

Tahmassebpour, Mahmoudreza

doi:10.1109/access.2017.2708080

Cited by 28 publications

(12 citation statements)

References 2 publications

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“…This makes data storage flexible and scalable. A novel approach for effective storage of time‐series data is proposed to reduce the computation expense [77].…”

Section: Big Data Stages and Solution Approachesmentioning

confidence: 99%

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

et al. 2019

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Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive stateof-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.

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“…This makes data storage flexible and scalable. A novel approach for effective storage of time‐series data is proposed to reduce the computation expense [77].…”

Section: Big Data Stages and Solution Approachesmentioning

confidence: 99%

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

et al. 2019

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“…They also identified the system correlation in terms of "Mean Spectral Radius (MSR)". The researchers in [11] implemented the Chornos Software (a kind of time-based database) in C++ language. They claim to increase the processing efficiency by 40-90 percent by storing the data and algorithms in RAM instead of main memory when compared with MongoDB and MYSQL databases.…”

Section: Related Workmentioning

confidence: 99%

Sorted r-Train: An Improved Dynamic Data Structure for Handling Big Data

Ahad¹,

Biswas²

2018

IJISA

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In today's computing era, the world is dealing with big data which has enormously expanded in terms of 7Vs (volume, velocity, veracity, variability, value, variety, visualization). The conventional data structures like arrays, linked list, trees, graphs etc. are not able to effectively handle these big data. Therefore new and dynamic tools and techniques which can handle these big data effectively and efficiently are the need of the hour. This paper aims to provide an enhancement to the recently proposed "dynamic" data structure "r-Train" for handling big data. With the emergence of the "Internet of Things (IoT)" technology, real-time handling of requests and services are pivotal. Therefore it becomes necessary to promptly fetch the required data as and when required from the enormous piles of big data that are generally located at different sites. Therefore an effective searching and retrieval mechanism must be provided that can handle these challenging issues. The primary aim of this proposed refinement is to provide an effective means of insertion, deletion and searching techniques to efficiently handle the big data.

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“…The Wiener filter method is only applicable to static processes. It is very difficult to establish an accurate state equation with the Kalman filtering method because it is necessary to understand the motion law of the system [22]- [25]. As a typical time and frequency analysis method, wavelet transform is widely used in the field of digital signal processing and is especially suitable for analyzing and processing non-stationary spectral signals.…”

Section: Introductionmentioning

confidence: 99%

Signal Enhancement Algorithm for On-Line Detection of Multi-Metal Ions Based on Ultraviolet-visible Spectroscopy

Zhou

Zhu

et al. 2020

IEEE Access

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In zinc hydrometallurgy, the traditional multi-metal ion detection method is manual off-line detection, which is cumbersome and has a long detection period. In order to realize simple, real-time, and accurate on-line detection, an adaptive signal enhancement algorithm combined with partial least squares regression is proposed for on-line detection of multi-metal ions by Ultraviolet-visible spectroscopy. First, according to the different scale characteristics of signal and noise in wavelet decomposition, the proposed signal enhancement algorithm can adaptively set the initial threshold, step size, and quantization ratio information using the magnitude of the signal and noise wavelet coefficients. Then, by increasing the threshold, the noise wavelet coefficient is set to zero and the signal wavelet coefficient is retained. When the sum of the wavelet coefficients slowly changes and tends to be stable, the optimal threshold parameter of each layer can be determined. Finally, the proposed adaptive algorithm is used for spectral signal preprocessing, and partial least squares regression is used for spectral signal modeling analysis. The simulation results showed that the proposed adaptive algorithm was superior to other denoising algorithms and had a better denoising effect under different noise backgrounds. The experimental results showed that the proposed adaptive algorithm combined with partial least squares regression was suitable for on-line detection of copper and cobalt in zinc hydrometallurgy, allowing it to have many applications. INDEX TERMS Zinc hydrometallurgy, signal enhancement algorithm, partial least squares, on-line detection, optimal threshold parameter, ultraviolet-visible spectroscopy.

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A New Method for Time-Series Big Data Effective Storage

Cited by 28 publications

References 2 publications

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Sorted r-Train: An Improved Dynamic Data Structure for Handling Big Data

Signal Enhancement Algorithm for On-Line Detection of Multi-Metal Ions Based on Ultraviolet-visible Spectroscopy

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