Fuzzy Approach to Data Reliability (original) (raw)

A systematic approach to reliability assessment in integrated databases

We provide a novel framework based on a systematic treatment of data inconsistency and the related concept of data reliability in integrated databases. Our main contribution is the formalization of reliability assessment for historical data where redundancy and inconsistency are common. We discover data inconsistency through the analysis of relationships between existing reports in the integrated database. We present a new approach by defining properties (rules) that a good measure of reliability should satisfy. We then propose such measures and show which properties they satisfy. We also report on a simulation-based study of the introduced framework.

DOI:10.2298/CSIS100102010S Towards the Methodology for Development of Fuzzy Relational Database Applications

2014

Abstract. In this paper we examine the possibilities to extend the relational data model with the mechanisms that can handle imprecise, uncertain and inconsistent attribute values using fuzzy logic and fuzzy sets. We present a fuzzy relational data model which we use for fuzzy knowledge representation in relational databases that guarantees the model in 3 rd normal form. We also describe the CASE tool for the fuzzy database model development which is apparently the first implementation of such a CASE tool. In this sense, this paper presents a leap forward towards the specification of a methodology for fuzzy relational database applications development.

The Survey Paper on Evaluating Data Reliability: An Evidential Answer with Application to a Web-Enabled Data Warehouse

2014

There are many available methods to integrate information source reliability in an uncertainty representation, but there are only a few works focusing on the problem of evaluating this reliability. However, data reliability and confidence are essential components of a data warehousing system, as they influence subsequent retrieval and analysis. In this paper, we propose a generic method to assess data reliability from a set of criteria using the theory of belief functions. Customizable criteria and insightful decisions are provided. The chosen illustrative example comes from real-world data issued from the Sym’Previus predictive microbiology oriented data warehouse.

Extending Relational Database Model for Uncertain Information

Journal of Computer Science and Cybernetics

In this paper, we propose a new probabilistic relational database model, denote by PRDB, as an extension of the classical relational database model where the uncertainty of relational attribute values and tuples are respectively represented by finite sets and probability intervals. A probabilistic interpretation of binary relations on finite sets is proposed for the computation of their probability measures. The combination strategies on probability intervals are employed to combine attribute values and compute uncertain membership degrees of tuples in a relation. The fundamental concepts of the classical relational database model are extended and generalized for PRDB. Then, the probabilistic relational algebraic operations are formally defined accordingly in PRDB. In addition, a set of the properties of the algebraic operations in this new model also are formulated and proven.