154 citations · 182 across the 14 of their papers we have counts for
20 papers
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…
Truly Sub- Min-Sum Subset Convolution and Join Ordering
Mihail Stoian
We present a deterministic reduction from min-sum subset convolution to min-plus matrix product. We show that if the min-plus product of two matrices with -bit integ…
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…
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…
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…
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…
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
Mihail Stoian, Mark Gerarts, Pascal Ginter +3
Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management…
The Case for Cardinality Lower Bounds
Mihail Stoian, Tiemo Bang, Hangdong Zhao +3
Despite decades of research, cardinality estimation remains the optimizer's Achilles heel, with industrial-strength systems exhibiting a systemic tendency toward underestimation. A…
Redbench: Workload Synthesis From Cloud Traces
Johannes Wehrstein, Roman Heinrich, Mihail Stoian +5
Workload traces from cloud data warehouse providers reveal that standard benchmarks such as TPC-H and TPC-DS fail to capture key characteristics of real-world workloads, including…
Instance-Optimized String Fingerprints
Mihail Stoian, Johannes Thürauf, Andreas Zimmerer +2
Recent research found that cloud data warehouses are text-heavy. However, their capabilities for efficiently processing string columns remain limited, relying primarily on techniqu…
Parachute: Single-Pass Bi-Directional Information Passing
Mihail Stoian, Andreas Zimmerer, Skander Krid +4
Sideways information passing is a well-known technique for mitigating the impact of large build sides in a database query plan. As currently implemented in production systems, side…
Redbench: A Benchmark Reflecting Real Workloads
Skander Krid, Mihail Stoian, Andreas Kipf
Instance-optimized components have made their way into production systems. To some extent, this adoption is due to the characteristics of customer workloads, which can be individua…
Lightweight Correlation-Aware Table Compression
Mihail Stoian, Alexander van Renen, Jan Kobiolka +3
The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios.…
DPconv: Super-Polynomially Faster Join Ordering
Mihail Stoian, Andreas Kipf
We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of . This is prohibitively expensive for larg…
Corra: Correlation-Aware Column Compression
Hanwen Liu, Mihail Stoian, Alexander van Renen +1
Column encoding schemes have witnessed a spark of interest with the rise of open storage formats (like Parquet) in data lakes in modern cloud deployments. This is not surprising --…
On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond
Mihail Stoian, Richard Milbradt, Christian B. Mendl
The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contrac…
Towards Practical Learned Indexing
Mihail Stoian, Andreas Kipf, Ryan Marcus +1
Latest research proposes to replace existing index structures with learned models. However, current learned indexes tend to have many hyperparameters, often do not provide any erro…
Benchmarking Learned Indexes
Ryan Marcus, Andreas Kipf, Alexander van Renen +5
Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified bench…
RadixSpline: A Single-Pass Learned Index
Andreas Kipf, Ryan Marcus, Alexander van Renen +4
Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learn…
SOSD: A Benchmark for Learned Indexes
Andreas Kipf, Ryan Marcus, Alexander van Renen +4
A groundswell of recent work has focused on improving data management systems with learned components. Specifically, work on learned index structures has proposed replacing traditi…