An efficient query optimization technique in big data using $$\sigma$$-ANFIS load balancer and CaM-BW optimizer

Kumar, Deepak; Jha, Vijay Kumar

doi:10.1007/s11227-021-03793-6

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In [57], Mirtaheri and Grandinetti discussed the importance of optimized load balancing in highperformance computing for Big Data analytics. The importance of load balancing and optimization for Big Data application was also identified by Kumar and Kumar Jha [58].…”

Section: Enabling Technologies For Big Datamentioning

confidence: 97%

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: 97%