Mihail Stoian

The Case for Synthesized Data Compression

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 or arithmetic encoding), leading to a zoo of general-purpose algorithms which work well for many datasets, but may not be optimal for any s...

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

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

This work makes the case that Parquet's default min-max metadata is enough to enable pruning, and proposes an enhanced metadata structure: a size-bounded 2D convex hull that verification tools can make better use of, increasing the pruning effectiveness to 38.31%, while occupying at most 45 bytes per row group and colu...

Hollywood: Towards a Large Movie Dataset for Database Benchmarking

Hollywood is introduced, a synthetic IMDb-compatible benchmark generator that combines LLM-generated semantic dictionaries with deterministic temporal-graph-based relational data generation and demonstrates that Hollywood induces cardinality estimation errors comparable to or exceeding those observed on the original IM...