BIGQA: Declarative Big Data Quality Assessment

What is it about?

In simple terms, our article introduces a new framework called BIGQA for assessing big data quality. Assessing data quality in the big data domain can be complex and challenging, especially when dealing with large volumes of data. BIGQA aims to simplify this process by providing a flexible and declarative framework for various domains and contexts. With BIGQA, data domain experts and data management specialists can easily plan and execute data quality assessment operations at any stage of the data life cycle. The framework is designed to work efficiently in parallel or distributed computing environments, allowing for faster processing and improved scalability. One of the critical features of BIGQA is its ability to generate customized data quality reports tailored to specific needs. It employs optimized operators to handle big data and ensures high parallelism during execution. Additionally, it supports incremental data quality assessment, which means it can avoid reprocessing the entire dataset every time an assessment is needed, saving time and resources. To validate the effectiveness of BIGQA, we conducted experiments using real-world data, including radiation wireless sensor data and Stack Overflow users' data. The results showed significant performance improvements compared to non-parallel and non-distributed approaches. For example, we achieved a 71% performance improvement on a 1 GB dataset compared to a non-parallel application and a 75% improvement on a 25 GB dataset in a distributed environment compared to a non-distributed application.

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Photo by NASA on Unsplash

Photo by NASA on Unsplash

Why is it important?

Our work on "BIGQA: Declarative Big Data Quality Assessment" stands out for its unique approach to addressing the complex task of assessing data quality in the context of big data. What makes our framework, BIGQA, distinct is its ability to provide a flexible and declarative solution that can be implemented in distributed and timely environments. This is crucial in today's data-driven world, where organizations are grappling with massive volumes of data and need efficient methods to ensure data quality. The timeliness of our work is evident in the growing prominence of big data and its impact on decision-making processes across industries. As the importance of data quality assessment continues to rise, our framework offers a fresh perspective and practical solutions for addressing this challenge. By introducing BIGQA, we aim to make a significant difference in the field of data quality assessment. The framework simplifies and generalizes the quality assessment operations, enabling data domain experts and data management specialists to plan and execute assessments efficiently at any stage of the data life cycle. The ability to generate customized data quality reports and handle big data with high parallelism further enhances its value. Our work has the potential to increase readership by attracting researchers, data scientists, and industry professionals who are seeking innovative approaches to tackle data quality challenges in the era of big data. The framework's flexibility, scalability, and performance improvements demonstrated in our experiments make it a compelling solution for improving data quality assessment processes. Furthermore, the practical applications of BIGQA across various domains and contexts make it relevant to a wide range of readers. Whether in healthcare, finance, marketing, or other sectors dealing with big data, our framework can provide actionable insights and drive more informed decision-making.

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