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All Journal The Eastasouth Journal of Information System and Computer Science
Chowdhury, Tanay
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Computer Science & IT

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Cloud-Based Information Retrieval for Big Data: A Survey of Architectures and Scalability Challenge Chowdhury, Tanay
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i03.937

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.
Semantic Search with Vector Database: A Comprehensive Review of Models, Indexing and Applications Chowdhury, Tanay
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i03.938

Abstract

The use of semantic search with the help of vector databases has become an impressive paradigm of retrieving the pertinent information by offering the contextual and conceptual sense of the information searching more than using the conventional methods of keyword searching. This paper provides an in-depth overview of the models of vector representation, transformer-based semantic encoders, and technologies of vectors database that jointly allow efficient and error-free semantic search. Classical distributional semantics, word-level embeddings, and transformer architectures are presented as background methods of making designed generating meaningful vectors representations. The paper also looks at the contemporary databases of vectors and indexing mechanisms which enable scalable similarity search in high-dimensional data. Moreover, different distance measures, hash algorithms and indexing strategies based on graphs are evaluated to determine how they can be used to maximize retrieval. Lastly, the paper presents practical examples of semantic searching with the use of the vector databases with text, image, audio and conversational applications, outlining both the main challenges and research opportunities.
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