Cognitive Decision-Making through an Intelligent Database Agent for Predictive Analysis
Abstract
Cognitive decision-making is an intellectual process aimed at the selection of a course of action among various choices. Information technology applications that support decision-making processes had an evolutionary transformation of spreadsheet software to the complex decision-support systems. The cognitive process of decision-making is being practiced through various intelligent information systems. The intelligent decision support system is being used by decision makers to make smart decisions in real-time dynamic environment based on intelligent data. To achieve intelligent data, it is needed to transform existing relational data and big data into agent-oriented data and subsequent design of data model to be in tune to the intelligent decision support system. For such system, the intelligent database agent (IDA) is designed to generate the analytical data for cognitive decision-making. The intelligent database agent is based on predictive data analytics. In any organization, predictive models utilize the sample which is found in historical and real-time transactional data to identify threats and prospects. IDA confines associations between many aspects to permit evaluation of threat or prospective associated with a particular set of conditions, steering cognitive decision-making. The development phases for the intelligent database agent is demonstrated and implemented to retrieve 64 the information for cognitive decision-making in the proposed framework. The proposed fram…
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AI-driven optimisation has shown a performance improvement of 25%, especially for adaptive indexing, self-tuning query execution plans, and anomaly detection in workload patterns.
“One of the most promising areas is AI-driven optimisation, which has shown a performance improvement of 25%, especially when it comes to adaptive indexing, self-tuning query execution plans and anomaly detection in workload patterns (Trivedi & Jobdas, 2021;Khan et al., 2023).”
From A REVIEW ON OBJECT-ORIENTED, OBJECT-RELATIONAL, RELATIONAL, AND NOSQL DATABASES: EVOLUTION, INTEGRATION, AND PERFORMANCE TRADE-OFFS (2025) · cites this paper as Trivedi & Jobdas, 2021
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Okoronkwo, M. C., Mustapha, M., Markus, C., Adaji, M. A., & Chibunze, N. E. (2025) · Open Journal of Physical Science (ISSN: 2734-2123) · doi:10.52417/ojps.v6i2.1039
- Authors
- Trivedi, S. A., & Jobdas, R.
- Venue
- Cognitive Computing Systems
- Publisher
- Apple Academic Press
- Published
- 2021-04-02
- Pages
- 63-95
- OpenAlex
- W3130028780
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Apple Academic Press eBooks (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Keywords
- cognitive decision-making, intelligent decision support system, predictive analytics, data model design, big data, transaction data, query execution time, scalability
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