Data Obsolescence Detection in the Light of Newly Acquired Valid Observations

TL;DR

This paper introduces a Bayesian network-based method for real-time detection of obsolete information in dynamic databases, effectively handling contradictions and improving decision-making in uncertain environments.

Contribution

It proposes a novel $ ext{ extsterling}$-Contradiction concept and a polynomial-time algorithm for detecting obsolete data, with practical application to elderly fall-prevention data.

Findings

Effective real-time contradiction detection in uncertain environments

Obsolete information better represented by AND-OR trees

Demonstrated success on elderly fall-prevention database

Abstract

The information describing the conditions of a system or a person is constantly evolving and may become obsolete and contradict other information. A database, therefore, must be consistently updated upon the acquisition of new valid observations that contradict obsolete ones contained in the database. In this paper, we propose a novel approach for dealing with the information obsolescence problem. Our approach aims to detect, in real-time, contradictions between observations and then identify the obsolete ones, given a representation model. Since we work within an uncertain environment characterized by the lack of information, we choose to use a Bayesian network as our representation model and propose a new approximate concept, $\epsilon$-Contradiction. The new concept is parameterised by a confidence level of having a contradiction in a set of observations. We propose a polynomial-time algorithm for detecting obsolete information. We show that the resulting obsolete information is better represented by an AND-OR tree than a simple set of observations. Finally, we demonstrate the effectiveness of our approach on a real elderly fall-prevention database and showcase how this tree can be used to give reliable recommendations to doctors. Our experiments give systematically and substantially very good results.