Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems
Summary: arXiv:2604.04939v1 Announce Type: new
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.
Key Features of the Proposed Proximity Measure
- Quantitative and Qualitative Analysis: The measure integrates both quantitative and qualitative data, providing a comprehensive approach to information object identification.
- Probabilistic Framework: A probabilistic measure is utilized for quantitative features to better handle variations in data.
- Possibility Measures: For qualitative features, a possibility measure is implemented, allowing for nuanced analysis of non-numeric data.
- Axiom Compliance: The proposed measure adheres to fundamental axioms necessary for any reliable proximity measure, ensuring its validity and applicability.
- No Transformation Required: Unlike existing measures, this approach does not necessitate the transformation of feature values, simplifying the comparison process.
Implications for Information Systems
The introduction of this proximity measure has significant implications for information systems, particularly in fields where data is collected from multiple sources. By accurately determining the relationships between various features of information objects, organizations can improve their data management practices and enhance the reliability of their information systems.
Furthermore, the ability to handle discrepancies in data values due to measurement errors can lead to better decision-making processes, especially in critical applications such as healthcare, finance, and security. The proposed approach offers a robust framework for identifying and correlating information objects, ultimately contributing to the advancement of data science methodologies.
Future Directions
Future research could focus on the following areas:
- Expanding the range of qualitative measures applicable to diverse feature sets.
- Integrating machine learning techniques to automate the identification process.
- Conducting empirical studies to validate the effectiveness of the proposed measure in real-world applications.
- Exploring the implications of this measure in big data environments.
In conclusion, the proposed quantitative-qualitative proximity measure provides a promising tool for addressing the challenges of identifying information objects in complex information systems. Its unique approach and adherence to fundamental axioms mark a significant advancement in the field.