A survey on quality-assurance approximate stream processing and applications

Abstract The massive growth of data now being made available from a variety of sources leads to an increased demand for fast data processing to extract value from the data. In data streams, processing data requires computational power and data storage capabilities that have not kept pace with the data collection abilities. For these reasons approximate computations have been developed to handle both computational issues as well as the storage issues especially related to real-time data streams. In this paper, we first propose a comprehensive study of approximate computing techniques for data streams. We classify common approximate techniques as data-driven and computing-driven methods, and also discuss the combination of the two methods in emerging distributed processing environments. Based on existing approximate methods, we then detail the research on data quality management including the quality evaluation and monitoring. The challenges to be faced are grouped into several research themes including pre-evaluation, data learning, approximation processing, and quality measurement. The aim of the paper is to provide researches with a guide for how to make effective systematic strategies for approximate stream processing.