arXiv subjects

Arno Kesper

Publications and source records attributed to Arno Kesper.

6 recordsLinked to original sources

arXiv2026

Domain-Specific Data Quality Analysis Using Technology-Independent Query Templates

In an increasingly data-driven world, effectively working with data depends heavily on its quality. Quality analysis is a central aspect of data quality management. As data quality is typically domain- and context-specific, the definition of quality requirements is primarily the responsibility of domain experts. However, domain experts often lack the query language expertise needed to implement quality analyses. Therefore, the process of defining quality analyses results in a resource-intensive workflow that requires the involvement of technical experts, effectively excluding domain experts from independently managing data quality. To address this challenge, we present the Quality Pattern Model framework (QPM), a model-driven approach to define templates for data quality analyses that are independent of specific database technologies and application domains. QPM can eliminate the need for deep technical expertise and prevent the need for defining quality analyses several times for different database technologies. We present a proof-of-concept implementation of this approach for three database technologies: XML, RDF, and Neo4j. We evaluate the expressiveness of our approach, its applicability in the cultural heritage domain, and its usability by domain experts. For this purpose, we conducted a qualitative user study and empirically collected quality problems in a catalog. Our findings suggest that QPM matches and even exceeds the expressiveness of common database query languages. Furthermore, the results indicate that our tool enables domain experts to define template-based quality analyses independently, without requiring support of IT experts.

cs.DB↗

arXiv2026

A Model-Driven Pipeline for Data Quality Specification and Operationalization: A No-Code Approach for Domain Experts

High-quality data is essential for reliable analysis, decision-making, and research across domains. This is especially relevant in areas such as cultural heritage, where data is collected and curated manually, making it prone to quality issues like inconsistencies. To improve data quality, the data must be analyzed regularly using systematic quality analyses. Quality analyses validate the conformance of data to domain-specific expectations. These expectations are best understood by domain experts, who can express them using natural language. However, they rarely possess the technical expertise to formalize these expectations into executable quality analyses. Consequently, this process requires domain experts and data engineers, making it time-consuming and technically demanding. The required technical expertise and the resulting dependencies pose a significant challenge. To address this challenge, we present a pipeline for formalizing and operationalizing data quality constraints. We support this pipeline using QPM, a metamodel for defining templates for reusable quality analyses. The web application Constrainify enables tailoring templates to specific conceptual requirements and translating them into executable quality analyses via a tool-chain based on model-driven engineering subpipelines. The result is a set of reusable, repeatable, and domain-specific quality analyses.

cs.IR↗

arXiv2026

Quality of Descriptive Information on Cultural Heritage Objects: Definition and Empirical Evaluation

Effective data processing depends on the quality of the underlying data. However, quality issues such as inconsistencies and uncertainties, can significantly impede the processing and subsequent use of data. Despite the centrality of data quality to a wide range of computational tasks, there is currently no broadly accepted, domain-independent consensus on the definition of data quality. Existing frameworks primarily define data quality in ways that are tailored to specific domains, data types, or contexts of use. Although quality assessment frameworks exist for specific domains, such as electronic health record data and linked data, corresponding approaches for descriptive information about cultural heritage objects remain underdeveloped. Moreover, existing quality definitions are often theoretical in nature and lack empirical validation based on real-world data problems. In this paper, we address these limitations by first defining a set of quality dimensions specifically designed to capture the characteristics of descriptive information about cultural heritage objects. Our definition is based on an in-depth analysis of existing dimensions and is illustrated through domain-specific examples. We then evaluate the practical applicability of our proposed quality definition using a curated set of real-world data quality problems from the cultural heritage domain. This empirical evaluation substantiates our definition of data quality, resulting in a comprehensive definition of data quality in this domain.

cs.DB↗

arXiv2025

How to Define the Quality of Data? A Feature-Based Literature Survey

The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A systematic literature review of the existing literature shows that data quality is a multifaceted concept, characterized by a number of quality dimensions. However, the definitions of data quality vary widely. We used feature-oriented domain analysis to specify a taxonomy of data quality definitions and to classify the existing definitions. This allows us to identify research gaps and future topics.

cs.DB↗

arXiv2021

Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values

Data is of high quality if it is fit for its intended use. The quality of data is influenced by the underlying data model and its quality. One major quality problem is the heterogeneity of data as quality aspects such as understandability and interoperability are impaired. This heterogeneity may be caused by quality problems in the data model. Data heterogeneity can occur in particular when the information given is not structured enough and just captured in data values, often due to missing or non-suitable structure in the underlying data model. We propose a bottom-up approach to detecting quality problems in data models that manifest in heterogeneous data values. It supports an explorative analysis of the existing data and can be configured by domain experts according to their domain knowledge. All values of a selected data field are clustered by syntactic similarity. Thereby an overview of the data values' diversity in syntax is provided. It shall help domain experts to understand how the data model is used in practice and to derive potential quality problems of the data model. We outline a proof-of-concept implementation and evaluate our approach using cultural heritage data.

cs.LG↗

arXiv2020

Detecting Quality Problems in Research Data: A Model-Driven Approach

As scientific progress highly depends on the quality of research data, there are strict requirements for data quality coming from the scientific community. A major challenge in data quality assurance is to localise quality problems that are inherent to data. Due to the dynamic digitalisation in specific scientific fields, especially the humanities, different database technologies and data formats may be used in rather short terms to gain experiences. We present a model-driven approach to analyse the quality of research data. It allows abstracting from the underlying database technology. Based on the observation that many quality problems show anti-patterns, a data engineer formulates analysis patterns that are generic concerning the database format and technology. A domain expert chooses a pattern that has been adapted to a specific database technology and concretises it for a domain-specific database format. The resulting concrete patterns are used by data analysts to locate quality problems in their databases. As proof of concept, we implemented tool support that realises this approach for XML databases. We evaluated our approach concerning expressiveness and performance in the domain of cultural heritage based on a qualitative study on quality problems occurring in cultural heritage data.

cs.IR↗