Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems

TL;DR

This paper introduces a new proximity measure for information object features that accounts for data discrepancies and does not require feature value transformation, aiding in identifying related objects across sources.

Contribution

It proposes a novel combined quantitative-qualitative proximity measure that handles errors and differences without feature transformation, enhancing object identification in information systems.

Findings

The measure complies with necessary axioms for validity.

It effectively handles both quantitative and qualitative feature differences.

Multiple variants of the proximity measure are proposed for diverse feature groups.

Abstract

The paper considers a new quantitative-qualitative proximity measure for the features of information objects, where data enters a common information resource from several sources independently. The goal is to determine the possibility of their relation to the same physical object (observation object). The proposed measure accounts for the possibility of differences in individual feature values - both quantitative and qualitative - caused by existing determination errors. To analyze the proximity of quantitative feature values, the author employs a probabilistic measure; for qualitative features, a measure of possibility is used. The paper demonstrates the feasibility of the proposed measure by checking its compliance with the axioms required of any measure. Unlike many known measures, the proposed approach does not require feature value transformation to ensure comparability. The work also proposes several variants of measures to determine the proximity of information objects (IO) based on a group of diverse features.