Big Data Analytics and Data Mining for Healthcare Informatics (HCI)

Varshney, Manasvi; Bhushan, Bharat; Haque, A. K. M. Bhalul

doi:10.1007/978-981-19-0924-5_11

Cited by 9 publications

(3 citation statements)

References 90 publications

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“…One promising area as an open challenge in Healthcare is Data Mining. Data mining is a potentially fruitful topic that remains fraught with difficulties in the healthcare realm [107], [163]. Integrating and analyzing data from several disparate sources is a substantial obstacle in the field of healthcare data mining as we discussed in the multimodality challenge.…”

Section: B Data Mining In Healthcarementioning

confidence: 99%

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Ahmed,

Xi,

Hou

et al. 2023

IEEE Access

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Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decisionmaking processes.

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Section: B Data Mining In Healthcarementioning

confidence: 99%

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Ahmed,

Xi,

Hou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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“…MapReduce facilitates distributed computing in the cluster and leverages the file processing capabilities provided by HDFS to enable task initialization, scheduling, and execution. 2,3,4 One of the significant advantages of Hadoop is its ability to be deployed on inexpensive commodity hardware clusters, eliminating the need for supercomputers. Hadoop encapsulates the complexity of its underlying implementation details, providing a stable API interface for developers to build applications on top of it.…”

Section: Introductionmentioning

confidence: 99%

Increased Task Execution with a Bandwidth-Aware Hadoop Scheduling Approach

R.Kennady

2023

IJRITCC

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This research presents a novel bandwidth-aware Hadoop scheduling method that addresses the challenge of task scheduling in Hadoop clusters while considering the real-time network conditions. The proposed method involves the establishment of a job time completion model and a mathematical model for a Hadoop scheduling system. Furthermore, it transforms the Hadoop task scheduling problem into an optimization problem to find the task scheduling method that minimizes job completion time. By leveraging Software-Defined Networking (SDN) capabilities, a time slot-based network bandwidth allocation mechanism is introduced to allocate bandwidth fairly across network links. The proposed method also takes into account task locality and network bandwidth availability when allocating computational nodes for individual tasks. Through this approach, the limitations of existing methods, which fail to simultaneously consider global task scheduling and actual network bandwidth availability, are overcome. Experimental evaluations demonstrate the effectiveness of the proposed method in enhancing the performance of Hadoop task scheduling.

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“…Moreover, a wide range of external tools and frameworks, such as HBase, Hive, and Zookeeper, complement Hadoop and extend its capabilities. 1,2 File system monitoring poses several challenges, including the need for effective monitoring, minimizing the impact on the distributed system's operation, and processing the resulting information. However, monitoring the distributed file system in Hadoop applications remains an open problem.…”

Section: Introductionmentioning

confidence: 99%

Hadoop-based File Monitoring System for Processing Image Data

O.Pandithurai

2023

IJRITCC

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This paper presents a file monitoring system based on the Hadoop framework, specifically designed for image data processing. The system comprises a Hadoop cluster and a client, where the Hadoop cluster includes various modules such as a name node module, a name node agent module, data node modules, a matching module, and a response algorithm module. The name node agent module acts as an intermediary between the client and the name node module, forwarding function information and acquiring configuration information. The system provides comprehensive monitoring capabilities for the distributed file system, enabling real-time handling of requests and messages.

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Big Data Analytics and Data Mining for Healthcare Informatics (HCI)

Cited by 9 publications

References 90 publications

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Increased Task Execution with a Bandwidth-Aware Hadoop Scheduling Approach

Hadoop-based File Monitoring System for Processing Image Data

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