arXiv subjects

Valerie Restat

Publications and source records attributed to Valerie Restat.

6 recordsLinked to original sources

arXiv2026

Rethinking Accuracy: A Weighted Error-Based Metric for Data Quality

Real data often contains errors, which is why data engineers spend a lot of time creating data cleaning pipelines to ensure the best possible data quality. However, it is often difficult to compare the results of different pipelines and decide which pipeline leads to the best results. There are many different metrics that are designed for different use cases, but they often take only a portion of the data into account. There is a lack of universally applicable metrics for measuring data quality that can be used in many different scenarios. That is why in this paper we are presenting TOMME - an initial approach to a universally applicable weighted error-based metric for data quality. This allows the data quality of a dataset to be assessed based on a single score. While a detailed data quality evaluation remains important, the use of a single score enables rapid assessment and automated processing, for example, for optimization algorithms. By using different weights, the score can also be precisely adjusted to the specific use case. That is why we named it TOMME, which stands for "The One Metric Measuring Errors". As the name suggests, it measures errors in the data. It can thus be considered a generalized, weighted form of accuracy.

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arXiv2026

Extending GouDa: Generation of Universal Datasets with (and without) Errors for Data Quality Benchmarking

Synthetic data is extremely important in areas such as data quality, data cleaning, and machine learning. It enables the analysis of use cases in which real data is insufficient, unavailable, or distorted. However, generating synthetic data also presents challenges: The data must be as realistic as possible, but at the same time cover edge cases. It must be possible to insert controlled errors, and at the same time, an error-free version of the data is usually required. Additionally, it is necessary to consider numerous data formats, such as tabular data, but also NoSQL data models. To this end, we present our data generator GouDa. GouDa precisely meets these requirements - it is suitable for different data formats, enables the controlled insertion of errors, and generates ground truth. A wide range of different generation functions and the option to add your own lists of possible attribute values allow the generation of realistic data that covers many different use cases.

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arXiv2025

Towards Next Generation Data Engineering Pipelines

Data engineering pipelines are a widespread way to provide high-quality data for all kinds of data science applications. However, numerous challenges still remain in the composition and operation of such pipelines. Data engineering pipelines do not always deliver high-quality data. By default, they are also not reactive to changes. When new data is coming in which deviates from prior data, the pipeline could crash or output undesired results. We therefore envision three levels of next generation data engineering pipelines: optimized data pipelines, self-aware data pipelines, and self-adapting data pipelines. Pipeline optimization addresses the composition of operators and their parametrization in order to achieve the highest possible data quality. Self-aware data engineering pipelines enable a continuous monitoring of its current state, notifying data engineers on significant changes. Self-adapting data engineering pipelines are then even able to automatically react to those changes. We propose approaches to achieve each of these levels.

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arXiv2025

MVIAnalyzer: A Holistic Approach to Analyze Missing Value Imputation

Missing values often limit the usage of data analysis or cause falsification of results. Therefore, methods of missing value imputation (MVI) are of great significance. However, in general, there is no universal, fair MVI method for different tasks. This work thus places MVI in the overall context of data analysis. For this purpose, we present the MVIAnalyzer, a generic framework for a holistic analysis of MVI. It considers the overall process up to the application and analysis of machine learning methods. The associated software is provided and can be used by other researchers for their own analyses. To this end, it further includes a missing value simulation with consideration of relevant parameters. The application of the MVIAnalyzer is demonstrated on data with different characteristics. An evaluation of the results shows the possibilities and limitations of different MVI methods. Since MVI is a very complex topic with different influencing variables, this paper additionally illustrates how the analysis can be supported by visualizations.

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arXiv2025

Data Cleaning of Data Streams

Streaming data can arise from a variety of contexts. Important use cases are continuous sensor measurements such as temperature, light or radiation values. In the process, streaming data may also contain data errors that should be cleaned before further use. Many studies from science and practice focus on data cleaning in a static context. However, in terms of data cleaning, streaming data has particularities that distinguish it from static data. In this paper, we have therefore undertaken an intensive exploration of data cleaning of data streams. We provide a detailed analysis of the applicability of data cleaning to data streams. Our theoretical considerations are evaluated in comprehensive experiments. Using a prototype framework, we show that cleaning is not consistent when working with data streams. An additional contribution is the investigation of requirements for streaming technologies in context of data cleaning.

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arXiv2023

Towards "all-inclusive" Data Preparation to ensure Data Quality

Data preparation, especially data cleaning, is very important to ensure data quality and to improve the output of automated decision systems. Since there is no single tool that covers all steps required, a combination of tools -- namely a data preparation pipeline -- is required. Such process comes with a number of challenges. We outline the challenges and describe the different tasks we want to analyze in our future research to address these. A test data generator which we implemented to constitute the basis for our future work will also be introduced in detail.

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