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20182026
most citedLearned Cardinalities: Estimating Correlated Joins with Deep Learning

162 citations · 403 across the 28 of their papers we have counts for

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8 papers · 1 filter

cs.DB2026

High-Throughput LLM-Based Compression and What It Takes: A Systems Study of Inference Engines and Entropy Coding Optimizations

David Adams, Luisa Neubauer, Ping-Lin Kuo +5

Large language models are attractive text compressors because next-token probabilities can be converted into code lengths. In practice, however, the main bottleneck is often not mo…

cs.DB2026

The Case for Synthesized Data Compression

Ivan Iachnyk, Mihail Stoian, Luisa Neubauer +2

Compression is a fundamental tool in data management. Today's compression schemes are generally designed by hand to apply to many different types of data (e.g., run length encoding…

cs.DB2026

OptFSST: Optimized FSST String Compression

Hedi Chehaidar, Mihail Stoian, Moritz Stargalla +1

Strings account for a substantial fraction of data in modern analytical systems, making lightweight compression with fast random access an important building block for efficient qu…

cs.DB2026

IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning

Yiyan Li, Guanli Liu, Renata Borovica-Gajic +6

Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, o…

cs.DB2026

Hollywood: Towards a Large Movie Dataset for Database Benchmarking

Ivan Iachnyk, Mihail Stoian, Andreas Kipf

The IMDb real-world dataset of the JOB benchmark has been extensively used in the last decade as part of the research line on cardinality estimation, given its ability to stress te…

cs.DB2026

SemCEB: A Cardinality Estimation Benchmark for Semantic Operators

Andreas Zimmerer, Claudius Kühn, Yang Li +3

Modern data systems increasingly expose multi-modal large language models as semantic operators: SQL operators, including filters and joins, whose predicates are defined by a natur…