Big Data Analytics: A Comparison of Tools and Applications

Alaoui, Imane El; Gahi, Youssef; Messoussi, Rochdi; Todoskoff, Alexis; Kobi, Abdessamad

doi:10.1007/978-3-319-74500-8_54

Cited by 13 publications

(3 citation statements)

References 51 publications

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“…Of the 7-Vs currently identified as the characteristics that define Big Data ( El Alaoui, Gahi, Messoussi, Todoskoff, & Kobi, 2017 ; Gandomi & Haider, 2015 ), our data is characterised by: volume , working with millions of data that allow a high level of representation; velocity , the data are updated daily, which allows us to quickly detect changes in behaviour; variability , referring to the variation in data flow rates; veracity , high degree of veracity of the information received as it is real data and not estimates or extrapolations; visualisation, the data is understandable and easy to read for the end user; and value , the data is transformed into useful information for decision making. We acknowledge that our data has limitations with respect to the characteristic “variety” that also defines Big Data.…”

Section: Discussionmentioning

confidence: 99%

The impact of COVID-19 on European tourists' attitudes to air travel and the consequences for tourist destination evoked set formation

Gallego

Font

González‐Rodríguez

2022

Tourism Management Perspectives

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Section: Discussionmentioning

confidence: 99%

The impact of COVID-19 on European tourists' attitudes to air travel and the consequences for tourist destination evoked set formation

Gallego

Font

González‐Rodríguez

2022

Tourism Management Perspectives

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“…Also, in addition to the dataset volume concern, the above approaches did not address the particular issues raised in a big data context. Indeed, with the emergence of big data, new challenges have been raised, such as the diversity of data sources, the variety of data types, and the high velocity and veracity of data [12] [13]. These particular issues were discussed in recent studies, such as in [14], where the authors have performed a survey of the indexing techniques for big data deduplication.…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Big Data Deduplication Framework based on Online Continuous Learning

Elouataoui¹,

Alaoui²,

Mendili³

et al. 2022

IJACSA

Self Cite

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While big data benefits are numerous, most of the collected data is of poor quality and, therefore, cannot be effectively used as it is. One pre-processing the leading big data quality challenges is data duplication. Indeed, the gathered big data are usually messy and may contain duplicated records. The process of detecting and eliminating duplicated records is known as Deduplication, or Entity Resolution or also Record Linkage. Data deduplication has been widely discussed in the literature, and multiple deduplication approaches were suggested. However, few efforts have been made to address deduplication issues in Big Data Context. Also, the existing big data deduplication approaches are not handling the case of the decreasing performance of the deduplication model during the serving. In addition, most current methods are limited to duplicate detection, which is part of the deduplication process. Therefore, we aim through this paper to propose an End-to-End Big Data Deduplication Framework based on a semi-supervised learning approach that outperforms the existing big data deduplication approaches with an F-score of 98,21%, a Precision of 98,24% and a Recall of 96,48%. Moreover, the suggested framework encompasses all data deduplication phases, including data preprocessing and preparation, automated data labeling, duplicate detection, data cleaning, and an auditing and monitoring phase. This last phase is based on an online continual learning strategy for big data deduplication that allows addressing the decreasing performance of the deduplication model during the serving. The obtained results have shown that the suggested continual learning strategy has increased the model accuracy by 1,16%. Furthermore, we apply the proposed framework to three different datasets and compare its performance against the existing deduplication models. Finally, the results are discussed, conclusions are made, and future work directions are highlighted.

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“…Therefore, data quality has gained increasing attention from researchers and practitioners in recent years due to its significant impact on organizations. Thus, numerous approaches and techniques have been proposed in the literature to address data quality issues [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis

Elouataoui,

El Mendili,

Gahi

2023

Data

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Big data has emerged as a fundamental component in various domains, enabling organizations to extract valuable insights and make informed decisions. However, ensuring data quality is crucial for effectively using big data. Thus, big data quality has been gaining more attention in recent years by researchers and practitioners due to its significant impact on decision-making processes. However, existing studies addressing data quality anomalies often have a limited scope, concentrating on specific aspects such as outliers or inconsistencies. Moreover, many approaches are context-specific, lacking a generic solution applicable across different domains. To the best of our knowledge, no existing framework currently automatically addresses quality anomalies comprehensively and generically, considering all aspects of data quality. To fill the gaps in the field, we propose a sophisticated framework that automatically corrects big data quality anomalies using an intelligent predictive model. The proposed framework comprehensively addresses the main aspects of data quality by considering six key quality dimensions: Accuracy, Completeness, Conformity, Uniqueness, Consistency, and Readability. Moreover, the framework is not correlated to a specific field and is designed to be applicable across various areas, offering a generic approach to address data quality anomalies. The proposed framework was implemented on two datasets and has achieved an accuracy of 98.22%. Moreover, the results have shown that the framework has allowed the data quality to be boosted to a great score, reaching 99%, with an improvement rate of up to 14.76% of the quality score.

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Big Data Analytics: A Comparison of Tools and Applications

Cited by 13 publications

References 51 publications

The impact of COVID-19 on European tourists' attitudes to air travel and the consequences for tourist destination evoked set formation

The impact of COVID-19 on European tourists' attitudes to air travel and the consequences for tourist destination evoked set formation

An End-to-End Big Data Deduplication Framework based on Online Continuous Learning

An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis

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