Gapprox: using Gallup approach for approximation in Big Data processing

Ahmadvand, Hossein; Goudarzi, Maziar; Foroutan, Fouzhan

doi:10.1186/s40537-019-0185-4

Cited by 33 publications

(31 citation statements)

References 41 publications

(34 reference statements)

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“…So, regardless to the category of applications there are several parameters such as data arrival rate, cluster size, tuple size and data type [43] which severely effect different performance metrics. Further, giving a recommendation for a framework per each category can vary depending on the desired metric.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

2019

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Large number of embedded devices, massive volumes of data, users and applications are driving the digital world to move faster than ever. To be competitive in today's digital economy companies have to process large volumes of dynamically changing data at real-time. There are many industries from health-care, e-commerce, insurance and telecommunications with various use cases such as DNA sequencing, capturing customer insights, real-time offers, high-frequency trading, and real-time intrusion detection that have taken the use of Big Data analytics into account to make critical decisions that impact their business [1]. On the other hand, the Internet of Things (IoT) is becoming the primary grounds for data mining and Big Data analytics [2]. With the rapid growth of IoT and its use cases in different domains such as Smart City, Mobile e-Health and Smart Grid, streaming applications are driving a new wave of data revolutions. In most IoT applications the resulting analytics give some feedbacks to the system to improve it [3]. Compared to the other Big Data domains, there is a low-latency cycle between system

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Section: Resultsmentioning

confidence: 99%

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

2019

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“…This method is more precise but could have the disadvantage of slowing down the ML computational times. Ahmadvand et al (2019) suggest the sampling of the computing techniques as an alternative. They add that this usually generates the desired quality of result when resources such as time, cost or energy are limited.…”

Section: Computational Timementioning

confidence: 99%

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

Grimaldi

Díaz

Arboleda

2020

Preprint

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Abstract The avalanche of personal and social data circulating in Online Social Networks over the past 10 years has attracted a great deal of interest from Scholars and Practitioners who seek to analyse not only their value, but also their limits. Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine Learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.

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“…They have not considered the data variety/ skew in their study. We have also considered data variety and reduced the processing resources such as energy or cost [21], [22].…”

Section: Fig 4 the Categories Of Related Workmentioning

confidence: 99%

“…• In this paper, we have presented an approach for reduction energy consumption in Big data processing for accumulative applications. We have presented the definition of accumulative application in [22]. This type of applications is an important type of Big Data applications [2], [22].…”

Section: Fig 13 Sensitivity Analysis To Deadlinementioning

confidence: 99%

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DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Ahmadvand

Foroutan

2020

Preprint

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Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in consumption of processing resources such as CPU consumption. In this paper, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider a deadline as our constraint and before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. We have used a set of data sets and applications in the evaluation phase. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

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Gapprox: using Gallup approach for approximation in Big Data processing

Cited by 33 publications

References 41 publications

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

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