{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:50:38Z","timestamp":1775663438916,"version":"3.50.1"},"reference-count":174,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this work, we present a principled framework for the deployment of Large Language Models (LLMs) in enterprise big data management across digital governance, marketing, and accounting domains. Unlike conventional predictive applications, our approach integrates LLMs as auditable, sector-adaptive components that robustly and directly enhance data curation, lineage, and regulatory compliance. The study contributes (i) a systematic evaluation of seven LLM-enabled functions\u2014including schema mapping, entity resolution, and document extraction\u2014that directly improve data quality and operational governance; (ii) a distributed architecture that deploys Apache Spark orchestration with Markov Chain Monte Carlo sampling to achieve quantifiable uncertainty and reproducible audit trails; and (iii) a cross-sector analysis demonstrating robust semantic accuracy, compliance management, and explainable outputs suited to diverse assurance requirements. Empirical evaluations reveal that the proposed architecture persistently attains elevated mapping precision, resilient multimodal feature extraction, and consistent human supervision. These characteristics collectively reinforce the integrity, accountability, and transparency of information ecosystems, particularly within compliance-driven organizational settings.<\/jats:p>","DOI":"10.3390\/a18120791","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T08:46:52Z","timestamp":1765874812000},"page":"791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["LLM-Driven Big Data Management Across Digital Governance, Marketing, and Accounting: A Spark-Orchestrated Framework"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4632-6511","authenticated-orcid":false,"given":"Aristeidis","family":"Karras","sequence":"first","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0891-6780","authenticated-orcid":false,"given":"Leonidas","family":"Theodorakopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4253-7661","authenticated-orcid":false,"given":"Christos","family":"Karras","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0008-547X","authenticated-orcid":false,"given":"George A.","family":"Krimpas","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9413-8841","authenticated-orcid":false,"given":"Anastasios","family":"Giannaros","sequence":"additional","affiliation":[{"name":"Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8837-2248","authenticated-orcid":false,"given":"Charalampos-Panagiotis","family":"Bakalis","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, M., Ji, Z., Luo, Z., Wu, Y., and Chai, C. 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