Inductive reasoning for significant concept and pattern discovery in cognitive IoT

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Service oriented computing and applications Ročník 19; číslo 3; s. 209 - 224
Hlavní autoři: Jha, Vidyapati, Tripathi, Priyanka
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.09.2025
Springer Nature B.V
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1863-2386
1863-2394
DOI:10.1007/s11761-024-00416-9