ABSTRACT
The amount of digital data is expanding rapidly because of the quick advancements in digital technologies. As a result, several sources, including social media, smartphones, sensors, etc., produce a lot of data. Emerging technologies like the Internet of Things (IoT) and recent developments in sensor networks have allowed for the collection of vast amounts of data. Such vast kinds of data that cannot be stored and processed by traditional relational databases and analytical methods are called big data. More effective techniques with high analytical accuracy are required for the investigation of such vast amounts of data. It is consequently necessary to develop fresh tools and analytical methods to find patterns from massive datasets. Big data is swiftly created from a variety of sources and formats. To effectively leverage quickly changing data, novel analytical methods must now be able to identify correlations between them. In big data analytics, artificial intelligence (AI) techniques like machine learning, knowledge-based, and decision-making algorithms can produce results that are more accurate, quicker, and scalable. Despite this interest, we are not aware of any comprehensive analysis of the various artificial intelligence algorithms for big data analytics. The main objective of the current survey is to examine artificial intelligence-based big data analytics research. Well-known databases 96like ScienceDirect, IEEEXplore, ACM Digital Library, and SpringerLink are used to choose relevant research articles. “Big data Analytics”, “Artificial intelligence”, “Big data Analytics” and “Machine Learning”, keywords are used to search the related research papers for the period 2017–2022. The AI techniques which are used to investigate the research papers are knowledge based, machine learning, search methods, and decision-making categories. In each category, several articles are investigated. This chapter also compares the selected AI-driven big data analytics techniques in terms of scalability, precision, efficiency, and privacy.
