A comparison of big remote sensing data processing with Hadoop MapReduce and Spark

Chebbi, Imen; Boulila, ‪Wadii; Mellouli, Nédra; Lamolle, Myriam; Farah, Imed Riadh

doi:10.1109/atsip.2018.8364497

Cited by 33 publications

(12 citation statements)

References 10 publications

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“…In addition, results in Tables 6-8 show that the proposed approach runs in a good manner for all instances with instances of diversity. As a perpective to this work will be to apply the proposed approach in the context of remote sensing big data [41][42][43][44], to explore the context of case-based reasoning using 'hyper-heuristic' [45,46], and to evaluate the effect of uncertainty in the process of search for nurse rostering problem [47][48][49].…”

Section: Resultsmentioning

confidence: 99%

A novel population-based local search for nurse rostering problem

Abuhamdah

Boulila

Jaradat

et al. 2021

IJECE

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Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.

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Section: Resultsmentioning

confidence: 99%

A novel population-based local search for nurse rostering problem

Abuhamdah

Boulila

Jaradat

et al. 2021

IJECE

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show abstract

“…To speed up the processing of big data, the proposed approach in Reference 24 relies on a Hadoop MapReduce framework. 33,34 Despite the high scalability of the proposed approach, the latter is applicable only to data that have linear correlation among the considered attributes, which is not true in certain areas of WSNs where measurements cannot be presented linearly.…”

Section: Related Workmentioning

confidence: 99%

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

et al. 2021

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Wireless sensor networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high-noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision-making processes in the base station.Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier-detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global-collaborative detection. Hence, the research presented in this article is intended to design and implement a global outlier-detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time-series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research Laboratory has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.

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“…Figure 5 illustrates the Temporal Chunk (TC) for an educational environment. [34]. Map Reducing is one of the most effective parallel programming platforms.…”

Section: Information Mining and Abstraction Layer (Imal)mentioning

confidence: 99%

IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

et al. 2021

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Education 4.0 contemplates the involvement of emerging technologies like IoT (Internet of Things), Fog and cloud computing. Ubiquitous real-time monitoring under the challenging environment of an educational institution is the key requirement for proper implementation of Education 4.0 around the world. Conspicuously, this research presents a novel monitoring and irregularity detection framework for educational institutions. In this research, acquisition of data while deploying IoT is being suggested in the comprehensive environs of the educational institutions. Pre-processing of acquired data for feature extraction is implemented using the Fog-Cloud nodes. Subsequently, the Temporal Chunk (TC)which is articulated with Temporal Data mining, is further employed to detect irregularities based on SoTL (Set of threshold limits)and HAA (Historical Adversity Approximation). Successively, Multi-Layered Bi-Directional Long Short Term Memory (M-Bi-LSTM) oriented irregularity prediction model is deployed. Furthermore, a fog-inspired alert generation and reporting module are employed for real-time reporting to notify the concerned stakeholder for in-time preventive action corresponding to predicted irregularity. To authenticate the proposed framework and to avoid the experimental implementation cost, the experimental simulations are performed. The experimental results verify that the proposed framework is capable to perform better in comparison to other contemporary decision-making methods for Delay Efficiency, Data Classification, Irregularity Prediction and System Stability. Moreover, the proposed framework can successfully estimate most of the irregularities in an educational environment and provide support for the proper implementation of Education 4.0 in a proficient and realistic manner.

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A comparison of big remote sensing data processing with Hadoop MapReduce and Spark

Cited by 33 publications

References 10 publications

A novel population-based local search for nurse rostering problem

A novel population-based local search for nurse rostering problem

Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

IoT Inspired Intelligent Monitoring and Reporting Framework for Education 4.0

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