Title: RSSCE: a SAM-based MapReduce framework for mining uncertain big data in cloud environments

Authors: Huiguo Dong; Jing Zhang; Qinlu He

Addresses: Information Engineering Department, Hebei Vocational University of Technology and Engineering, Xingtai, Hebei, China ' Information Engineering Department, Hebei Vocational University of Technology and Engineering, Xingtai, Hebei, China ' College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi, China

Abstract: The exponential growth of data generation - encompassing text, images and videos - through social media and digital platforms has introduced significant challenges in extracting meaningful insights. While big data presents numerous opportunities, leveraging it effectively for business and decision-making remains a key focus of information technology. Data retrieval aims to transform vast, unstructured data sets into valuable information, with particular emphasis on identifying frequent patterns - repeated items, substructures or sequences that meet user-defined thresholds. Owing to the immense data volume, users often operate on specific subsets, thereby limiting the search space. To address this, a technique utilising the Search Area Minimisation (SAM) approach integrates user-defined constraints to narrow the search field and highlight relevant patterns. Experimental results across deterministic and uncertain data sets. This proposed approach provides a scalable and reliable solution for frequent pattern mining in big data environments, enabling focused and effective analysis based on user preferences and uncertainty management.

Keywords: big data; uncertain data; frequent pattern extraction; search space reduction; user constraints; MapReduce framework; SAM technique; data analysis; non-deterministic databases.

DOI: 10.1504/IJIPT.2026.153846

International Journal of Internet Protocol Technology, 2026 Vol.19 No.2, pp.77 - 85

Received: 15 Apr 2025
Accepted: 02 Jun 2025
Published online: 28 May 2026 *

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