Cloud computing and big data: Technologies and applications

Zbakh, Mostapha; Bakhouya, Mohamed; Essaaidi, Mohamed; Manneback, Pierre

doi:10.1002/cpe.4517

Cited by 6 publications

(1 citation statement)

References 24 publications

(31 reference statements)

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“…In [74], Hashem et al discussed the relation between Big Data and cloud computing, including research challenges. Zbakh et al were the editors for a special issue on cloud computing and Big Data [75]. The manuscripts of that special issue focus on job scheduling, resource optimization, privacy and security and performance evaluation.…”

Section: Enabling Technologies For Big Datamentioning

confidence: 99%

Research Trends, Enabling Technologies and Application Areas for Big Data

Lundberg¹,

Grahn²

2022

Algorithms

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The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.

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Section: Enabling Technologies For Big Datamentioning

confidence: 99%

Research Trends, Enabling Technologies and Application Areas for Big Data

Lundberg¹,

Grahn²

2022

Algorithms

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Cloud computing, IoT, and big data: Technologies and applications

Bakhouya

Zbakh

Essaaidi

et al. 2020

Concurrency and Computation

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A novel visualization approach for data provenance

Yazici

Aktaş

2021

Concurrency and Computation

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Data provenance has led to a developing need for the technologies to empower end-users to assess and take action on the data life cycle. In the Big Data era, companies' amount of data over the world increases each day. As data increases, metadata on the data origin and lifecycle of data also overgrows. Thus, this requires innovations that can provide a better understanding and interpretation of data using data provenance. This study addresses the challenge of extracting data in the form of graphs from scientific workflows and facilitating demanded visualization approaches such as graph comparison, summarization, backward-forward querying, and stream data visualization. W3C-PROV-O provenance specification is implemented via a visualization tool to assess the applicability of proposed algorithms. The proposed algorithms are tested on a large-scale provenance dataset to explore their performance. In addition, this study discusses the details of a comprehensive usability study of the prototype visualization tool. Results indicate that proposed visualization approaches are usable and processing overhead is insignificant.

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Cloud computing and big data: Technologies and applications

Cited by 6 publications

References 24 publications

Research Trends, Enabling Technologies and Application Areas for Big Data

Research Trends, Enabling Technologies and Application Areas for Big Data

Cloud computing, IoT, and big data: Technologies and applications

A novel visualization approach for data provenance

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