Big Data Analytics Framework for System Health Monitoring

Xu, Brian; Kumar, Sathish A.P.

doi:10.1109/bigdatacongress.2015.66

Cited by 31 publications

(6 citation statements)

References 19 publications

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“…It is possible that notes that one of the obstacles is rapid development, which does not leave adequate time and assets to absorb the estimation outcomes within internet framework, applications, and conventions. We can understand the system foundation and individual patterns after testing them in isolated lab situations and system reproductions, even though a worldwide scale hostile internet environment is not clear [14].…”

Section: Internet Traffic Measurement and Analysismentioning

confidence: 99%

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Jacob¹,

Tharwat²,

Fudzee³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Internet traffic monitoring is to measure and analyze the network bottlenecks to manage the online data are transferring processes efficiently. Various tools have been developed by using internet traffic measurement and internet traffic analysis tools, such as Hadoop. Activity measurement and adaptive examination represent the dynamics of information exchange. On the other hand, information exchange and dynamics measure movement in light of the system assets that can be accessed depending on the characteristics of the exchanged information. The main aim of this work is to apply scalable features of internet traffic measurement and analysis using Hadoop to understand the effects of these features on the speed of transferring data. This gives a new vision or opportunity to dynamically adapting the most suitable traffic measurement and analysis feature according to network capabilities and environment. This research employs Hadoop/Map Reduce as scalable internet traffic measurement and analysis tools. The simulation was conducted by using five personal computers; one as a server and four virtual computers as network nodes. Each computer has 2GB memory and 100GB storage. Five types of data segmentation are utilized 10 MB, 40MB, 64MB, 200MB, and500MB. The speed of the network is calculating in a megabit per second (Mbs) based upon the network speed on the number of allocated PCs (100 Mbs/4). The simulation is conducted to test the data transfer time based on various selections of network capabilities such as transferring extensive data through a network of medium and heavy usage.

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Section: Internet Traffic Measurement and Analysismentioning

confidence: 99%

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Jacob¹,

Tharwat²,

Fudzee³

et al. 2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…For example, the model allows practitioners to identify population groups more likely to benefit from certain care plans (Li et al, 2015) [66]. Therefore, it can be used to improve resources allocation [79]. Healthcare providers can further use predictive modeling to determine disease patterns and their impact on various population groups based on characteristics such as geographical location, age, gender, or socioeconomic status.…”

Section: Opportunities Of Big Data In Health Carementioning

confidence: 99%

Impact of Big Data Analysis on Health

Rosário¹,

Dias²

2022

Preprint

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Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

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“…The adoption of machine learning is an obvious option for the analytics development but the challenges for developing machine learning models for the health care related big data are distinguished. In this direction, B. Xu et al [30] proposes an framework to the purpose of the analysis of the data from the distributed sources using machine learning but the aspects of the data purity as veracity factor is not considered.…”

Section: Related Workmentioning

confidence: 99%

Modeling Method for Leveraging Data Quality in Healthcare Big Data

K.*,

Ramesh

2020

IJITEE

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An accurate diagnosis of the healthcare-based Big Data will always demand a significant level of quality in its input data itself, which is a serious level of concern in the area of healthcare analytics. Review of existing approaches shows that there has been various learning-based approaches being used for disease diagnosis which often ignores various issues viz. data aggregation, presence of error prone data, accuracy etc. Therefore, this paper presents a novel framework which offers cost effective modeling of the aggregation process of healthcare-big data followed by facilitating solution towards identifying and rectifying all the positions within a database system where there are presence of an error. The proposed system offer a mechanism where the error-prone data has been identified and substituted with data of better quality in order to offer better analytical outcomes. The study offers a strong baseline in order to leverage the data quality in healthcare big data.

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Big Data Analytics Framework for System Health Monitoring

Cited by 31 publications

References 19 publications

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Analysis of How Scalable Features in Hadoop / MapReduce by Internet Traffic Management

Impact of Big Data Analysis on Health

Modeling Method for Leveraging Data Quality in Healthcare Big Data

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