Research Article
Optimization of Query Processing Through Nested Persistent Storage Implementation of Relational Tables
Issue:
Volume 10, Issue 3, September 2026
Pages:
82-100
Received:
8 June 2026
Accepted:
22 June 2026
Published:
28 July 2026
Abstract: Relational databases use physical design techniques such as indexing, clustering, and partitioning to improve performance without altering the logical schema seen by applications. This paper proposes nesting of persistent storage structures as a new physical optimisation technique. Related tables can be implemented as a single nested persistent storage structure, eliminating join operations when processing complex queries and removing redundant foreign key repetitions on the many side of the relationship. The paper describes two independent nesting strategies. The schema driven approach selects nesting candidates from the logical schema before any workload is known. This selection subject to storage optimisation and database organisation constraints, formulating the selection as a constrained maximum-weight branching problem. The workload driven approach determines nesting decisions subject to aggregate query processing frequencies, applying nesting transformations in descending order of join frequency. To preserve logical transparency, mapping metadata records each attribute's physical access path. The paper shows how SQL queries expressed over the logical schema can be automatically transformed into equivalent queries over the nested physical structures, keeping the nested representation hidden from applications. A prototype implementation in PostgreSQL on the TPC-H benchmark demonstrates the feasibility of the proposed approach and confirms that standard indexing techniques can recover the query performance overhead introduced by nesting. By reducing storage redundancy and eliminating join operations while remaining compatible with existing relational interfaces, the technique preserves the relational programming model.
Abstract: Relational databases use physical design techniques such as indexing, clustering, and partitioning to improve performance without altering the logical schema seen by applications. This paper proposes nesting of persistent storage structures as a new physical optimisation technique. Related tables can be implemented as a single nested persistent sto...
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Research Article
Artificial Intelligence in Library and Information Science Research: Trends, Citation Patterns, and Knowledge Structure Analysis
Vinayak Savatagi*
,
Umesha Naik
Issue:
Volume 10, Issue 3, September 2026
Pages:
101-111
Received:
1 September 2026
Accepted:
22 September 2026
Published:
9 October 2026
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are changing Library and Information Science (LIS) by impacting on user services, cataloguing, and information retrieval; however, the literature on how this is happening has not been systematically catalogued. To fill this gap, this study was conducted using a bibliometric analysis of the papers pertaining to AI, ML and LIS published from 2015 to 2024. The aim of the study is to analyse the growth of publication, citation trends, characteristics of authors, keyword co-occurrence networks, and international collaboration in this field to discover the intellectual structures and future trends of the field. A total of 2180 documents from 436 sources were retrieved by applying a structured keyword search strategy, after removing the duplicates and the irrelevant documents, the retrieved documents were analysed with the help of the Bibliometrix package in RStudio and VOSviewer for descriptive bibliometric analysis and network-based bibliometric analysis respectively. Results indicate an increase at an annual rate of 43.16%, with only 2024 reporting 707 publications or 32.43% of the total. The data set contained 7,623 authors, each of whom authored an average of 3.84 co-authors per document, and only 71 documents (3.26%) had a single author, showing a very collaborative research culture. The most common and most cited author keywords were “machine learning” (7,633 citations), “deep learning” (4,182), and “learning systems” (3,272). The document with the most citations garnered 351 citations, with India becoming the top among the contributing countries and the largest node in the international co-authorship networks. From these results, it can be inferred that AI/ML research in LIS is growing rapidly, is collaborative in nature and has a higher focus on machine learning and deep learning applications. Based on the study, the researcher concludes that this bibliometric mapping will be a baseline reference for researchers, librarians and policy makers to understand the evolution of AI and ML in LIS and to identify emerging research areas like generative AI, explainable AI and intelligent digital libraries.
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are changing Library and Information Science (LIS) by impacting on user services, cataloguing, and information retrieval; however, the literature on how this is happening has not been systematically catalogued. To fill this gap, this study was conducted using a bibliometric analysis of the pape...
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