A Comprehensive Study of Approximate Query Processing Techniques for Big Data Analytics

Abstract

In an era of exponential data growth, the need for fast and scalable query processing algorithms has increased. Traditional approaches, once a mainstay of data analysis, increasingly fail to provide quick results when querying across large data sets. Then Approximate Query Processing (AQP) comes into play, a hope for big data analysis. AQP points to a strategic pivot that prioritizes speed of query response over absolute precision. This shift has significant implications for real-time and interactive analytics, where speed is of the utmost importance. Our AQP trip is a guided tour around the planet. It analyzes numerous AQP strategies, offers a structured overview of their inner workings, classifies them according to important characteristics and critically evaluates their strengths and weaknesses. Additionally, we bridge theory and practice by highlighting specific use cases where AQP has made a lasting impression. From e-commerce and healthcare to finance and science, AQP has revolutionized data-driven decision-making across multiple industries. The aim of this study is to provide researchers, data scientists and industry experts with the knowledge and insights required to realize the potential of AQP in the era of big data. It provides a glimpse into the future of data analytics by capturing the core of AQP's role in balancing data accuracy and query performance.