Data Quality Assessment Methodology

Publications

Data Quality Assessment Methodology

Data Quality Assessment Methodology
Daniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei

Abstract. High-quality data is a precondition for reliable machine learning, analytics and decision support. This paper introduces a methodology for systematic data quality assessment that combines dimensions such as completeness, consistency, accuracy, timeliness and validity into a unified evaluation framework. The methodology defines reusable measurement procedures, scoring schemes and aggregation rules that can be applied at the level of individual fields, datasets or entire data pipelines. By providing a structured way to detect and quantify data quality issues, the proposed approach supports continuous monitoring, helps prioritise remediation actions and improves the trustworthiness of downstream data-driven services, with a particular focus on smart agriculture data ecosystems.

Keywords: data quality; data quality dimensions; assessment methodology; data governance; smart agriculture

📋 Cite this publication



Daniela Delinschi, Rudolf Erdei, Emil Pasca, Oliviu Matei, "Data Quality Assessment Methodology", Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024, 2023.


Reference: Proc. 19th SOCO Int. Conf. on Soft Computing Models in Industrial and Environmental Applications, Springer, 2024.

Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

,

Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper aims to model the monthly precipitation in 8 precipitation observation stations. The effects and role of different feature weights pre-processing methods (Weight by deviation, Weight by PCA, Weight by correlation, and Weight by Support Vector Machine) on artificial intelligence modeling were investigated.

read more
A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem

A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem

, ,

The clustered shortest-path tree (CluSPT) problem is an extension of the classical shortest path problem, given a graph with the nodes partitioned into several mutually exclusive and collectively exhaustive clusters looks for a shortest-path spanning tree from a predefined source node to all the other nodes of the graph, with the property that every cluster should generate a connected subgraph.

read more
A comprehensive survey on the generalized traveling salesman problem

A comprehensive survey on the generalized traveling salesman problem

, ,

The generalized traveling salesman problem (GTSP) is an extension of the classical traveling salesman
problem (TSP), and it is among the most researched combinatorial optimization problems due to its theoretical properties, complexity aspects, and real-life applications in various areas: location-routing problems, material flow design problem, distribution of medical supplies, urban waste collection management, airport selection and routing the courier airplanes, image retrieval and ranking, digital garment manufacturing, etc.

read more

Other publications

0 Comments