Cloud-Based Information Retrieval for Big Data: A Survey of Architectures and Scalability Challenge
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Abstract
Cloud computing has become a paradigm of managing, storing and retrieving large amounts of data emanating in contemporary digital applications. The mode of information retrieval (IR), which is typically insufficient in large-scale, heterogeneous, and dynamic data settings, has been severely challenged by the issue of big data, namely its high volume, high velocity, high diversity, high veracity, and high value. Cloud retrieval information systems take advantage of the elasticity, scalability and on-demand provisioning of cloud systems to facilitate effective and cost-effective access to data across distributed platforms. This work is a critical overview of the concept of big data and cloud-based IR, with a specific emphasis on the most significant models of cloud service, the specifics of data types, and the prospects of ML and DL to improve the quality of retrieval and relevance. Moreover, the paper logically examines key scalability issues, such as distributed storage management, index maintenance, query processing latency, load balancing and resource provisioning. All critical issues related to security and privacy, including leakage of data, insider threats, and vulnerability of programming interfaces, and multi-tenancy risks are also discussed. This paper, by summarizing the available literature and discovering gaps in the research, offers useful information on how scalable, secure, and intelligent information retrieval systems can be designed, as well as presents future research opportunities so as to facilitate reliable deployment of the system in data-intensive applications.
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