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

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 determi

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Volodymyr Yuzefovych · · 1 min read · 5 views
#cs.AI

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.

Executive Summary

This paper introduces a novel quantitative-qualitative proximity measure for identifying information objects (IOs) in multi-source data environments, addressing the challenge of associating disparate data entries with the same physical entity. The proposed measure integrates probabilistic methods for quantitative features and possibility theory for qualitative attributes, accommodating inherent determination errors without requiring feature transformation for comparability. The author validates the measure by demonstrating compliance with axiomatic requirements and extends the approach to group-based proximity measures for diverse feature sets. The work offers a theoretically robust and practical solution to the longstanding problem of IO identification in information systems.

Key Points

  • ▸ Introduces a hybrid proximity measure combining probabilistic and possibility-theoretic approaches to evaluate feature similarity in information objects.
  • ▸ Accommodates both quantitative and qualitative feature differences due to measurement errors without necessitating data normalization or transformation.
  • ▸ Validates the measure by verifying its compliance with axiomatic requirements and extends it to group-based proximity measures for heterogeneous feature sets.
  • ▸ Demonstrates feasibility through theoretical validation rather than empirical testing, leaving practical implementation and scalability as open questions.

Merits

Theoretical Rigor and Innovation

The paper presents a sophisticated fusion of probabilistic and possibility-theoretic frameworks, addressing a critical gap in multi-source data integration. The measure’s axiomatic validation ensures mathematical soundness, distinguishing it from ad hoc proximity measures.

Practical Relevance to Information Systems

The approach directly tackles real-world challenges in data fusion, such as entity resolution and deduplication, particularly in domains like healthcare, logistics, and IoT where data heterogeneity is prevalent.

Flexibility in Feature Handling

By avoiding feature transformation, the measure preserves the interpretability of raw data while enabling comparability across diverse feature types, a significant advantage over traditional methods requiring standardization.

Demerits

Lack of Empirical Validation

The paper relies solely on theoretical validation (axiomatic compliance) without demonstrating the measure’s performance on real-world datasets or comparative benchmarks against existing methods.

Assumption of Measurement Error Independence

The measure assumes that determination errors in feature values are independent across sources, which may not hold in practice (e.g., systemic biases in measurement tools or reporting errors).

Scalability Concerns for High-Dimensional Data

The computational complexity of the proposed group-based measures, particularly in large-scale systems with numerous features and sources, is not addressed and may pose practical limitations.

Expert Commentary

The paper presents a compelling theoretical contribution to the field of information systems, particularly in addressing the longstanding challenge of multi-source data integration. The fusion of probabilistic and possibility-theoretic measures is innovative and addresses a critical gap in handling both quantitative and qualitative feature differences without resorting to data transformation, which often distorts inherent data properties. The axiomatic validation ensures mathematical rigor, a commendable aspect often overlooked in applied research. However, the absence of empirical validation is a notable limitation, as the measure’s real-world applicability remains untested. For instance, in domains like healthcare, where patient data is often noisy and heterogeneous, the measure’s performance against existing methods (e.g., deterministic matching or machine learning-based approaches) would be illuminating. Additionally, the assumption of independent measurement errors may not hold in practice, and the scalability of the proposed group-based measures for high-dimensional data remains an open question. Future work should prioritize empirical validation across diverse datasets and integrate domain-specific constraints (e.g., temporal dynamics in sensor data) to enhance practical utility. The paper’s contributions are nonetheless foundational and merit further exploration by both theorists and practitioners.

Recommendations

  • ✓ Conduct empirical validation by testing the measure on real-world datasets across multiple domains (e.g., healthcare, logistics, IoT) to assess its performance against state-of-the-art methods in entity resolution and deduplication.
  • ✓ Develop scalable algorithms or approximations for the proposed group-based measures to address computational complexity in high-dimensional data environments, ensuring practical deployment in large-scale systems.
  • ✓ Extend the measure to incorporate temporal dynamics or sequential dependencies, particularly for streaming data or time-series applications where feature values evolve over time.
  • ✓ Explore hybrid approaches that combine the proposed measure with machine learning techniques (e.g., neural networks for feature weighting) to improve adaptability to domain-specific data characteristics.
  • ✓ Collaborate with standards bodies (e.g., ISO/IEC) to incorporate the measure into data quality frameworks, particularly for regulated industries where entity resolution accuracy is critical for compliance.

Sources

Original: arXiv - cs.AI

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