Version 1
: Received: 30 October 2024 / Approved: 31 October 2024 / Online: 31 October 2024 (15:21:51 CET)
How to cite:
Sayeed, S. A.; Kshetri, N.; Rahman, M. M.; Alam, S.; Rahman, A. DataLDA: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review. Preprints2024, 2024102596. https://doi.org/10.20944/preprints202410.2596.v1
Sayeed, S. A.; Kshetri, N.; Rahman, M. M.; Alam, S.; Rahman, A. DataLDA: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review. Preprints 2024, 2024102596. https://doi.org/10.20944/preprints202410.2596.v1
Sayeed, S. A.; Kshetri, N.; Rahman, M. M.; Alam, S.; Rahman, A. DataLDA: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review. Preprints2024, 2024102596. https://doi.org/10.20944/preprints202410.2596.v1
APA Style
Sayeed, S. A., Kshetri, N., Rahman, M. M., Alam, S., & Rahman, A. (2024). <em>DataLDA</em>: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review. Preprints. https://doi.org/10.20944/preprints202410.2596.v1
Chicago/Turabian Style
Sayeed, S. A., Samiul Alam and Abdur Rahman. 2024 "<em>DataLDA</em>: Analyzing Data Quality in Big Data Using Traditional Approaches vs. Latent Dirichlet Allocation - Systematic Review" Preprints. https://doi.org/10.20944/preprints202410.2596.v1
Abstract
Big data quality is all about ensuring that the huge amounts of data are accurate, reliable, and useful. It is important because decisions made based on this data can affect many things, like business strategies, customer experiences, and even public policies. Many researchers are studying data quality in the context of big data. This study aims to analyze the research trends and patterns within the domain of big data quality. This method reveals hidden themes in large datasets, helping to understand data quality better. The findings show that Latent Dirichlet Allocation (LDA) is effective for topic modeling in big data, highlighting data quality challenges and opportunities. The research identified key trends and effective assessment methods. For researchers, using LDA in a systematic review helps summarize existing studies and find gaps, guiding future research. Practitioners and future researchers get useful direction for managing data quality and improving efficiency and decisions in various industries.
Keywords
big data; data quality; topic modeling; latent dirichlet allocation (lda); systematic literature review
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
