Inductive reasoning for significant concept and pattern discovery in cognitive IoT

• Published in: Service oriented computing and applications Vol. 19; no. 3; pp. 209 - 224
• Main Authors: Jha, Vidyapati, Tripathi, Priyanka
• Format: Journal Article
• Language: English
• Published: London Springer London 01.09.2025; Springer Nature B.V
• Subjects: Algorithms; Automation; Cognition; Cognition & reasoning; Cognitive science; Computer Appl. in Administrative Data Processing; Computer Science; Computer Systems Organization and Communication Networks; Data analysis; Datasets; e-Commerce/e-business; Internet of Things; IT in Business; Kalman filters; Knowledge discovery; Logic; Machine learning; Management of Computing and Information Systems; Massive data points; Original Research Paper; Reasoning; Software Engineering/Programming and Operating Systems; Concept lattice; Reasoning; Concept and pattern discovery; Cognitive IoT
• ISSN: 1863-2386; 1863-2394
• DOI: 10.1007/s11761-024-00416-9

Recent research on the Internet of Things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces a new field known as Cognitive IoT (CIoT). Therefore, the CIoT inherits several features and challenges from IoT. The Cognitive IoT encompasses billions of devices that generate large amounts of heterogeneous, volatile, and time-dependent data. To ensure the smooth functioning of CIoT applications, meaningful insight must be obtained from the massive amounts of data. Thus, in order to uncover the hidden knowledge from these massive data sets, there needs to be a cognitively intelligent data analysis technique that is computationally efficient and cost-effective. Keeping this in mind, this research proposes inductive reasoning for extracting the concept and patterns from twenty-one years of environmental data. In the first phase of the proposed algorithm, the inductive value is computed for each chunk of the dataset, and it is transformed into a binary dataset for concept lattice generation. Furthermore, a weight assignment is performed for each generated concept, and the minimal inductive-valued concept is selected for inductive reasoning. Following the extraction of the generalized concept, the highest entropy row is selected by combining its corresponding concept data. As a result, this pattern is referred to as significant. An evaluation of the proposed algorithm on different scales demonstrates its efficiency over competing approaches.