3 citations · 9 across the 10 of their papers we have counts for
11 papers
Clean Me If You Can: A Large Collection of Real-World Addresses for Data Cleaning Benchmarking
Fatemeh Ahmadi, Tobias Bernhard, Mohamed Abdelmaksoud +3
There has been extensive research on automating and scaling data cleaning, i.e., the detection and correction of erroneous values in tabular data. Yet, existing approaches often pe…
RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms
Mohamed Abdelmaksoud, Sheng Ding, Andrey Morozov +1
Time-series data vary widely across domains, making a universal anomaly detector impractical. Methods that perform well on one dataset often fail to transfer because what counts as…
Quantifying Retriever-Generator Alignment in RAG with Local Explanations
Korbinian Randl, Guido Rocchietti, Aron Henriksson +3
Retrieval-Augmented Generation (RAG) systems combine dense retrievers and language models to ground outputs in external documents. However, the interaction between these components…
Guiding Catalogue Enrichment with User Queries
Yupei Du, Jacek Golebiowski, Philipp Schmidt +1
Techniques for knowledge graph (KGs) enrichment have been increasingly crucial for commercial applications that rely on evolving product catalogues. However, because of the huge se…
Blend: A Unified Data Discovery System
Mahdi Esmailoghli, Christoph Schnell, Renée J. Miller +1
Most research on data discovery has so far focused on improving individual discovery operators such as join, correlation, or union discovery. However, in practice, a combination of…
AutoML in Heavily Constrained Applications
Felix Neutatz, Marius Lindauer, Ziawasch Abedjan
Optimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hype…
Learning Action Embeddings for Off-Policy Evaluation
Matej Cief, Jacek Golebiowski, Philipp Schmidt +2
Off-policy evaluation (OPE) methods allow us to compute the expected reward of a policy by using the logged data collected by a different policy. OPE is a viable alternative to run…
MATE: Multi-Attribute Table Extraction
Mahdi Esmailoghli, Jorge-Arnulfo Quiané-Ruiz, Ziawasch Abedjan
A core operation in data discovery is to find joinable tables for a given table. Real-world tables include both unary and n-ary join keys. However, existing table discovery systems…
RPT: Effective and Efficient Retrieval of Program Translations from Big Code
Binger Chen, Ziawasch Abedjan
Program translation is a growing demand in software engineering. Manual program translation requires programming expertise in source and target language. One way to automate this p…
ED2: Two-stage Active Learning for Error Detection -- Technical Report
Felix Neutatz, Mohammad Mahdavi, Ziawasch Abedjan
Traditional error detection approaches require user-defined parameters and rules. Thus, the user has to know both the error detection system and the data. However, we can also form…
Unsupervised String Transformation Learning for Entity Consolidation
Dong Deng, Wenbo Tao, Ziawasch Abedjan +7
Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clus…