Temporal Event Tracing on Big Healthcare Data Analytics

Lin, Chinho; Huang, Liang-Cheng; Chou, Shu-Hsien; Liu, Chih-Ho; Cheng, Han-Fang; Chiang, I-Jen

doi:10.1109/bigdata.congress.2014.48

Cited by 30 publications

(10 citation statements)

References 16 publications

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“…We can also infer that accuracy mapping for actual and analyzed action is most prevalent in three baud rates such as 19 200, 11 520, and 2 000 000 bps. In contract to the related works, 69‐71 our study shows more promising approach toward solving streaming and analytics services at the constrained IoT‐edge environment. The scaling, analysis and mapping of glitching factor and pulse rate accuracy may be sought as a novel method that is not demonstrated by any literature until now 72‐74 .…”

Section: Evaluation and Discussionmentioning

confidence: 81%

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

Ray

Dash

Moustafa

2020

Trans Emerging Tel Tech

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Bio‐sensor data streaming and analytics is a key component of smart e‐healthcare. However, existing Internet of Things (IoT) ecosystem is unable to materialize the real‐time bio‐sensor data streaming and analytics within resource constrained environment. Moreover, traditional solutions fail to mitigate the edge‐cloud integration within a single sub‐system under IoT periphery which lead to investigate how edge‐cloud hybridization could be realized via similar set of tools. The objective of this article is to implement an integrated dual‐mode edge‐cloud system to serve streaming and analytics in real‐time. This study aims to achieve the aforesaid goal by presenting two different experiments that deals with the real‐time pulse sensor data streaming and analytics while utilizing light‐weight IoT‐supported JavaScript frameworks that includes Node.js, Johnny‐Five, Serialport.js, Plotly client, Flot.js, jQUERYy, Express Server, and Socket.io. Firstly, a standalone IoT‐edge system is developed and later, an integrated IoT‐based edge‐cloud system is developed to compare between the effectiveness of the systems. The implementation results show near correlation between the standalone edge and dual‐mode edge system. However, the dual‐mode edge‐cloud system provides more flexibility and capability to counter the bio‐sensor data streaming and analytics services within the constrained framework.

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Section: Evaluation and Discussionmentioning

confidence: 81%

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

Ray

Dash

Moustafa

2020

Trans Emerging Tel Tech

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“…In other words, claims data is designed to hold only those pieces of information that are required to facilitate payment by an insurance company: what service was provided, the diagnosis, who was the service provider, how much money is owed for that service. Further vital information is usually added, such as which types of health services were delivered, and the associated costs owed for the insurance company to process, among others [11] [21].…”

Section: Healthcare Insurance Datamentioning

confidence: 99%

A Mathematical Approach to Healthcare Insurance Data Analytics

Yange¹,

Gambo

Omodunbi

et al. 2021

jcsitech

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The emergence of big data analytics as a way of deriving insights from data has brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the near absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. A prototype for the model was implemented using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, it was tested for accuracy using data from the National Health Insurance Scheme in Nigeria with a view to reducing delays in the processes of the Scheme. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the flow of resources.

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“…After these, it will do compression and pre-processing of that data, according to the modules of application. Encryption module encrypts these health care data to ensure security of that information during transmission from unauthorized user [10]. Thus, unauthorized user can't decrypt these data and misuse these.…”

Section: Stages Of Models Information Collection Componentmentioning

confidence: 99%

Review on Smart Healthcare Systems using Cloud and Big Data Analysis

Junwale¹,

Tjprc²

2018

IJCSEITR

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Many researchers are working for decades, to enhance the quality and speed of analysis in health care systems. For improving treatment quality, Cyber-physical systems are used in biomedical field. Cyber-physical systems along with cloud computing techniques and big data analysis technology are used to improve quality and to efficiently handle the huge heterogeneous medical data. In this paper, health care information is collected from multisource, heterogeneous sources like hospitals, Internet, or user-generated content. These data is then cleaned and pre-processed and further given to management layer, which uses distributed file storage component and distributed parallel computing component, which enhances the speed and quality of heterogeneous data. Cloud computing technology improves economies of scale by resource sharing. Application service interface is used to manage and develop a uniform application program interface for users. The result of this study enhances quality of health care applications using cloud and big data analysis techniques.

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Temporal Event Tracing on Big Healthcare Data Analytics

Cited by 30 publications

References 16 publications

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective

A Mathematical Approach to Healthcare Insurance Data Analytics

Review on Smart Healthcare Systems using Cloud and Big Data Analysis

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