Advances in MapReduce Big Data Processing: Platform, Tools, and Algorithms

Abualigah, Laith; Masri, Bahaa Al

doi:10.1007/978-981-33-6400-4_6

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…Settings in the streaming paradigm have been developed and abstracted by researchers. On the one hand, MapReduce is unsuitable for expressing streaming algorithms [84]. On the other hand, conventional online algorithms are constrained by the memory and bandwidth of a single console.…”

Section: Tools For Big Data Analysismentioning

confidence: 99%

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

Dasari,

Kaluri

2023

Wireless Communications and Mobile Computing

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In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.

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Section: Tools For Big Data Analysismentioning

confidence: 99%