6 citations · 15 across the 12 of their papers we have counts for
6 papers · 1 filter
DataLens: ML-Oriented Interactive Tabular Data Quality Dashboard
Mohamed Abdelaal, Samuel Lokadjaja, Arne Kreuz +1
Maintaining high data quality is crucial for reliable data analysis and machine learning (ML). However, existing data quality management tools often lack automation, interactivity,…
Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Rieke Müller, Mohamed Abdelaal, Davor Stjelja
Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability. In response to this challenge, we present a mic…
LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs
Fabian Biester, Mohamed Abdelaal, Daniel Del Gaudio
Machine learning's influence is expanding rapidly, now integral to decision-making processes from corporate strategy to the advancements in Industry 4.0. The efficacy of Artificial…
AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
Mohamed Abdelaal, Rashmi Koparde, Harald Schoening
Machine learning algorithms have become increasingly prevalent in multiple domains, such as autonomous driving, healthcare, and finance. In such domains, data preparation remains a…
RTClean: Context-aware Tabular Data Cleaning using Real-time OFDs
Daniel Del Gaudio, Tim Schubert, Mohamed Abdelaal
Nowadays, machine learning plays a key role in developing plenty of applications, e.g., smart homes, smart medical assistance, and autonomous driving. A major challenge of these ap…
REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines
Mohamed Abdelaal, Christian Hammacher, Harald Schoening
Nowadays, machine learning (ML) plays a vital role in many aspects of our daily life. In essence, building well-performing ML applications requires the provision of high-quality da…