# Data Obsolescence Detection in the Light of Newly Acquired Valid Observations

arXiv: https://arxiv.org/abs/2101.07067 · HTML: https://mlsanity.com/paper/2101.07067/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 78 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [The Shapley Value of Tuples in Query Answering](https://mlsanity.com/paper/1904.08679/) (2019)
- [Generalized Evidence Theory](https://mlsanity.com/paper/1404.4801/) (2014)
- [The Shapley Value of Inconsistency Measures for Functional Dependencies](https://mlsanity.com/paper/2009.13819/) (2020)
- [Computing Optimal Repairs for Functional Dependencies](https://mlsanity.com/paper/1712.07705/) (2017)
- [Properties of Inconsistency Measures for Databases](https://mlsanity.com/paper/1904.06492/) (2019)

## Built on by (most cited first, 1 of 1)

- [Obsolete Personal Information Update System for the Prevention of Falls among Elderly Patients](https://mlsanity.com/paper/2101.10132/) (2021)

## Abstract (arXiv)

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, ε-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.