Etdc

Alotaibi, Saad B.

doi:10.1145/3133264.3133296

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…The proposed approach is an extension to improve the smart technique presented in [2], Which focused on the cleaning of some errors of datasets, such as redundant values, error formats, numeric errors of values, and Arabic mistakes in sentences. Many techniques that have applied to solve common problems and specific kinds of errors still limited.…”

Section: Introductionmentioning

confidence: 99%

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

Al-Hagery¹,

Alreshoodi²,

Almutairi³

et al. 2019

IJITCS

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Real-World datasets accumulated over a number of years tend to be incomplete, inconsistent and contain noisy data, this, in turn, will cause an inconsistency of data warehouses. Data owners are having hundred-millions to billions of records written in different languages, hence continuously increases the need for comprehensive, efficient techniques to maintain data consistency and increase its quality. It is known that the data cleaning is a very complex and difficult task, especially for the data written in Arabic as a complex language, where various types of unclean data can occur to the contents. For example, missing values, dummy values, redundant, inconsistent values, misspelling, and noisy data. The ultimate goal of this paper is to improve the data quality by cleaning the contents of Arabic datasets from various types of errors, to produce data for better analysis and highly accurate results. This, in turn, leads to discover correct patterns of knowledge and get an accurate Decision-Making. This approach established based on the merging of different algorithms. It ensures that reliable methods are used for data cleansing. This approach cleans the Arabic datasets based on the multi-level cleaning using Arabic Misspelling Detection, Correction Model (AMDCM), and Decision Tree Induction (DTI). This approach can solve the problems of Arabic language misspelling, cryptic values, dummy values, and unification of naming styles. A sample of data before and after cleaning errors presented. . Currently, he is teaching the master degree students and a supervisor of four master thesis. He is a jury member of a number of PhD and master thesis, as an internal and external examiner in his field of his specialist.

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Section: Introductionmentioning

confidence: 99%

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

Al-Hagery¹,

Alreshoodi²,

Almutairi³

et al. 2019

IJITCS

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A Thematic Review on Data Quality Challenges and Dimension in the Era of Big Data

Ridzuan

Zainon

Zairul

2021

Lecture Notes in Electrical Engineering

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Data quality is the primary concern faced by most of the organizations due to improper maintenance in the database. Data obtained from the various resources are dirty, affecting the accuracy of predicted results. There are a lot of challenges when handling Big Data because it requires well-defined and precise measurement processes. The challenges are in the characteristics of big data itself where the V's play an important role in measuring and determining data quality. Although the issue has been discussed over 20 years, there is no guideline in identifying the important dimension of data quality being proposed to adhere with the context of Big Data. Therefore, the purpose of this systematic review is to review literature on the issue, challenges, and dimension of data quality in the era of Big Data using thematic review. This review included journal and conference proceeding papers from ACM Digital Library, Scopus, and Science Direct published between 2016 until 2020. Inclusion and exclusion processes have filtered out 21 final articles for the review. A systematic review on these 21 articles focuses on the issue, challenges, and dimension of data quality. The results of this study benefit the future study on the development of data quality dimensions and can be a guideline for the researcher to design the data quality assessment framework.

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Comparison of Main Algorithms in Big Data Analysis of Telecom Customer Retention

Palaoag

Cruz

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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In today’s fierce telecommunications market competition, customer chum is very severe. In order to retain customers, telecommunications companies have made various attempts from various data and consumption characteristics analysis to big data analysis. However, since the actual situation of customer churn is very complicated, how to predict customer churn accurately and quickly is a difficult problem. After the researchers successfully conducted big data analysis of customer churn and successfully retained customers, in this article, the researchers mainly compared several commonly used algorithms in order to find a better algorithm for big data analysis of telecommunications customer churn. Compare and analyze the accuracy and efficiency of these several algorithms and suggest that the business support staffs of telecommunications companies adopt major methods for big data analysis. The researcher found that the Decision Tree (CART) algorithm is better for the prediction of customer churn and guided other branch staffs to predict customer churn and retain customers in a timely manner. This kind of big data analysis can be used to retain customers in the telecommunications industry.

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Etdc

Cited by 4 publications

References 9 publications

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

A hybrid Technique for Cleaning Missing and Misspelling Arabic Data in Data Warehouse

A Thematic Review on Data Quality Challenges and Dimension in the Era of Big Data

Comparison of Main Algorithms in Big Data Analysis of Telecom Customer Retention

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