AI, IoMT and Blockchain in Healthcare

Sarker, Bipasha; Sharif, Numair Bin; Rahman, Md. Ashiqur; Parvez, A. H. M. Shahariar

doi:10.36548/jtcsst.2023.1.003

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…It segments data into smaller chunks, processes them in parallel, and then aggregates the results. It's instrumental in tasks like analyzing patient records, identifying disease patterns, or processing claims data [20][21][22]. On the other hand, Apache Spark, with its in-memory processing capabilities, is ideal for iterative and interactive workloads.…”

Section: Spark Vs Mapreduce: Which Is Faster?mentioning

confidence: 99%

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

Sifat Ibtisum,

Ehsan Bazgir,

S M Atikur Rahman

et al. 2023

World J. Adv. Res. Rev.

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The paper addresses a highly relevant and contemporary topic in the field of data processing. Big data is a crucial aspect of modern computing, and the choice of processing framework can significantly impact performance and efficiency. The technical revolution of big data has changed how organizations handle and value large databases. As data quantities expand quickly, effective and scalable data processing systems are essential. MapReduce and Apache Spark are two of the most popular large data processing techniques. This study compares these two frameworks to determine their merits, shortcomings, and applicability for big data applications. Nearly quintillion bytes of data are created daily. Approximately 90% of data was produced in the previous two years. At this stage, data comes from temperature sensors, social media, movies, photographs, transaction records (like banking records), mobile phone conversations, GPS signals, etc. In this article, all key big data technologies are introduced. This document compares all big data technologies and discusses their merits and downsides. Run trials using multiple data sets of varying sizes to validate and explain the study. Graphical depiction shows how one tool outperforms others for given data. Big Data is data generated by the rapid usage of the internet, sensors, and heavy machinery, with great volume, velocity, variety, and veracity. Numbers, photos, videos, and text are omnipresent in every sector. Due to the pace and amount of data generation, the computing system struggles to manage large data. Data is stored in a distributed architectural file system due to its size and complexity. Big distributed file systems, which must be fault-tolerant, adaptable, and scalable, make complicated data analysis dangerous and time-consuming. Big data collection is called ‘datafication’. Big data is ‘datafied’ for productivity. Organisation alone does not make Big Data valuable; we must choose what we can do with it.

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Section: Spark Vs Mapreduce: Which Is Faster?mentioning

confidence: 99%

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

Sifat Ibtisum,

Ehsan Bazgir,

S M Atikur Rahman

et al. 2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Bzip2 is an advanced data compression algorithm based on libbzip2, distributed under an open-source (BSD-style license) [9][10][11][12][13]. It employs a technique similar to the PPM family, offering compression performance twice as fast and decompression six times faster than the older version 1.0.6.…”

Section: Bzip2mentioning

confidence: 99%

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Sifat Ibtisum,

S M Atikur Rahman,

S. M. Saokat Hossain

2023

World J. Adv. Res. Rev.

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This article conducts a thorough comparative analysis of Apache Tez and MapReduce in the context of big data processing. It focuses on key performance metrics, scalability, and ease of use. The analysis begins with an overview of the architectural distinctions between the two frameworks, emphasizing their fundamental design principles. A detailed performance evaluation follows, considering factors such as execution time, resource utilization, and throughput across diverse workloads. The study explores scalability by examining how Apache Tez and MapReduce respond to increasing data volumes and computational demands. Cluster size effects, resource allocation strategies, and adaptability to dynamic workloads are scrutinized. Additionally, the article evaluates the frameworks' ease of use for developers and administrators, incorporating aspects like programming model simplicity, debugging capabilities, and system configurability. User experiences are gathered through surveys and practical use cases. The conclusions drawn from this analysis offer valuable insights for organizations and practitioners seeking suitable distributed computing frameworks. By addressing both performance and user experience, the article aims to provide a comprehensive perspective on the strengths and weaknesses of Apache Tez and MapReduce, assisting decision-makers in making informed choices for their big data processing requirements.

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Healthcare Data Security Using AI and Blockchain: Safeguarding Sensitive Information for a Safer Society

Upadhyay,

Singh,

Kar

et al. 2024

Blockchain Technologies

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AI, IoMT and Blockchain in Healthcare

Cited by 4 publications

References 60 publications

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

Comparative analysis of MapReduce and Apache Tez Performance in Multinode clusters with data compression

Healthcare Data Security Using AI and Blockchain: Safeguarding Sensitive Information for a Safer Society

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