Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
A pipelined strategy for processing recursive queries in parallel
Data & Knowledge Engineering
Principles of Database Systems
Principles of Database Systems
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Knowledge Discovery in Databases
Knowledge Discovery in Databases
An Extended Relational Database Model for Uncertain and Imprecise Information
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
The role of domain knowledge in data mining
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Designing a Kernel for Data Mining
IEEE Expert: Intelligent Systems and Their Applications
SLFD Logic: Elimination of Data Redundancy in Knowledge Representation
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Artificial Intelligence Review
Non-deterministic ideal operators: An adequate tool for formalization in Data Bases
Discrete Applied Mathematics
Design science in information systems research
MIS Quarterly
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The problem of making decisions among propositions based on both uncertain data items and arguments which are not certain is addressed. The primary knowledge discovery issue addressed is a classification problem: which classification does the available evidence support? The method investigated seeks to exploit information available from conventional database systems, namely, the integrity assertions or data dependency information contained in the database. This information allows ranking arguments in terms of their strengths. As a step in the process of discovering classification knowledge, using a database as a secondary knowledge discovery exercise, latent knowledge pertinent to arguments of relevance to the purpose at hand is explicated. This is called evidence. Information is requested via user prompts from an evidential reasoner. It is fed as evidence to the reasoner. An object-oriented structure for managing evidence is used to model the conclusion space and to reflect the evidence structure. The implementation of the evidence structure and an example of its use are outlined.