Clustering based approach for incomplete data streams processing

Najib, Fatma M.; Ismail, Rasha M.; Badr, Nagwa L.; Gharib, Tarek F.

doi:10.3233/jifs-191184

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Najib et al [26] proposed the fuzzy c-means clustering framework to deal with missing values. In the evaluation, the results were compared with those of existing algorithms in terms of the average confusion matrix, average F-score, and average specificity with different rates of missing values.…”

Section: Machine Learningmentioning

confidence: 99%

Cleaning Big Data Streams: A Systematic Literature Review

2023

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In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overcome these challenges, such as cleaning big data in real time. This systematic literature review presents recently developed techniques that have been used for the cleaning process and for each data cleaning issue. Following the PRISMA framework, four databases are searched, namely IEEE Xplore, ACM Library, Scopus, and Science Direct, to select relevant studies. After selecting the relevant studies, we identify the techniques that have been utilized to clean big data streams and the evaluation methods that have been used to examine their efficiency. Also, we define the cleaning issues that may appear during the cleaning process, namely missing values, duplicated data, outliers, and irrelevant data. Based on our study, the future directions of cleaning big data streams are identified.

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Section: Machine Learningmentioning

confidence: 99%

Cleaning Big Data Streams: A Systematic Literature Review

2023

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An Efficient Approach for Query Processing of Incomplete High Dimensional Data Streams

Najib

Ismail

Badr

et al. 2021

Advances in Intelligent Systems and Computing

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A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach

Alotaibi,

Tomy,

Pardede

2024

Computers

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Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works.

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Clustering based approach for incomplete data streams processing

Cited by 4 publications

References 36 publications

Cleaning Big Data Streams: A Systematic Literature Review

Cleaning Big Data Streams: A Systematic Literature Review

An Efficient Approach for Query Processing of Incomplete High Dimensional Data Streams

A Framework for Cleaning Streaming Data in Healthcare: A Context and User-Supported Approach

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