Decision analysis using belief functions
International Journal of Approximate Reasoning
Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
Evaluating aggregates in possibilistic relational databases
Data & Knowledge Engineering
Answering heterogeneous database queries with degrees of uncertainty
Distributed and Parallel Databases
On decision making using belief functions
Advances in the Dempster-Shafer theory of evidence
Generalized union and project operations for pooling uncertain and imprecise information
Data & Knowledge Engineering
Fast discovery of association rules
Advances in knowledge discovery and data mining
Optimal and efficient integration of heterogeneous summary tables in a distributed database
Data & Knowledge Engineering
Using background knowledge in the aggregation of imprecise evidence in databases
Data & Knowledge Engineering
New Techniques and Technologies for Statistics II
New Techniques and Technologies for Statistics II
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Evidence Theory and Its Applications
The Management of Probabilistic Data
IEEE Transactions on Knowledge and Data Engineering
Evaluating Aggregate Operations Over Imprecise Data
IEEE Transactions on Knowledge and Data Engineering
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Aggregation of Imprecise and Uncertain Information in Databases
IEEE Transactions on Knowledge and Data Engineering
Designing a Kernel for Data Mining
IEEE Expert: Intelligent Systems and Their Applications
A Scalable Approach to Integrating Heterogeneous Aggregate Views of Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Inconsistency detection and resolution for context-aware middleware support
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
Information inconsistencies detection using a rule-map technique
Expert Systems with Applications: An International Journal
Partial constraint checking for context consistency in pervasive computing
ACM Transactions on Software Engineering and Methodology (TOSEM)
Energy-efficient query management scheme for a wireless sensor database system
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
An evidential approach to integrating semantically heterogeneous distributed databases
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Inference in possibilistic network classifiers under uncertain observations
Annals of Mathematics and Artificial Intelligence
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Information from which knowledge can be discovered is frequently distributed due to having been recorded at different times or to having arisen from different sources. Such information is often subject to both imprecision and uncertainty. The Dempster-Shafer representation of evidence offers a way of representing uncertainty in the presence of imprecision, and may therefore be used to provide a mechanism for storing imprecise and uncertain information in databases. We consider an extended relational data model that allows the imprecision and uncertainty associated with attribute values to be quantified using a mass function distribution. When a query is executed, it may be necessary to combine imprecise and uncertain data from distributed sources in order to answer that query. A mechanism is therefore required both for combining the data and for generating measures of uncertainty to be attached to the (imprecise) combined data. In this paper we provide such a mechanism based on aggregation of evidence. We show first how this mechanism can be used to resolve inconsistencies and hence provide an essential database capability to perform the operations necessary to respond to queries on imprecise and uncertain data. We go on to exploit the aggregation operator in an attribute-driven approach to provide information on properties of and patterns in the data. This is fundamental to rule discovery, and hence such an aggregation operator provides a facility that is a central requirement in providing a distributed information system with the capability to perform the operations necessary for Knowledge Discovery.