arXiv subjects

Kevin M. Kramer

Publications and source records attributed to Kevin M. Kramer.

3 recordsLinked to original sources

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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arXiv2023

Towards Evolution Capabilities in Data Pipelines

Evolutionary change over time in the context of data pipelines is certain, especially with regard to the structure and semantics of data as well as to the pipeline operators. Dealing with these changes, i.e. providing long-term maintenance, is costly. The present work explores the need for evolution capabilities within pipeline frameworks. In this context dealing with evolution is defined as a two-step process consisting of self-awareness and self-adaption. Furthermore, a conceptual requirements model is provided, which encompasses criteria for self-awareness and self-adaption as well as covering the dimensions data, operator, pipeline and environment. A lack of said capabilities in existing frameworks exposes a major gap. Filling this gap will be a significant contribution for practitioners and scientists alike. The present work envisions and lays the foundation for a framework which can handle evolutionary change.

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