arXiv subjects

Mihail Stoian

Publications and source records attributed to Mihail Stoian.

At least 19 recordsLinked to original sources

arXiv2026

Hollywood: Towards a Large Movie Dataset for Database Benchmarking

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 test both traditional and learned estimators. However, unlike the synthetic TPC family, it does not come with a scale factor, being a simple dump. We introduce Hollywood, a synthetic IMDb-compatible benchmark generator that combines LLM-generated semantic dictionaries with deterministic temporal-graph-based relational data generation. We analyze a preliminary Hollywood-200K, which contains 200,000 primary movies, generated series and episode title rows, 19.7M IMDb-style rows, and 213 nonzero JOB-Light, JOB, and JOB-Complex queries. Experiments with two open systems demonstrate that Hollywood induces cardinality estimation errors comparable to or exceeding those observed on the original IMDb dataset. The release includes generation settings and prompt/LLM-output provenance together with adapted SQL and labels, enabling tests of whether cardinality estimators generalize beyond a fixed movie snapshot and distribution.

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arXiv2026

OptFSST: Optimized FSST String Compression

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 query processing. Fast Static Symbol Table (FSST) addresses this need by replacing frequent byte sequences with compact codes while preserving independent decompression of individual strings. However, FSST's compression effectiveness is limited by its greedy symbol selection and greedy encoding strategy, leaving encoding gains on the table. We present OptFSST, an optimized FSST variant that improves its compression factors while preserving its static-symbol-table design and random-access decompression. OptFSST optimally encodes the text using dynamic programming given a symbol table. Additionally, we show that a generalized version of the symbol-table selection problem is NP-hard when the alphabet is part of the input, motivating heuristic table construction for field-level compressors. Hence, we add in OptFSST (i) an additional frequency counter that accelerates the discovery of longer symbols and (ii) a pruning strategy that removes redundant and conflicting symbol candidates during table construction. We also extend the same techniques to FSST12, yielding OptFSST12. Our evaluation on 92 real-world string datasets shows that OptFSST improves the compression factors of FSST and FSST12 by up to 47.7% and 91.5%, with an average improvement of 7.3% and 17.0%, respectively, while retaining the fine-grained random-access properties. Notably, OptFSST12 improves FSST12's decompression speed by $1.2\times$ on average.

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arXiv2026

SemCEB: A Cardinality Estimation Benchmark for Semantic Operators

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 natural-language instruction. Query optimization in these systems still rests on the same foundations as in traditional databases$\unicode{x2013}$plan enumeration and cost models$\unicode{x2013}$yet faces new challenges, e.g., a larger plan space and the lack of efficient cardinality estimates. The elevated per-tuple costs of semantic operators make bad plan choices worse by orders of magnitude. Therefore, precise$\unicode{x2013}$but also fast and cheap$\unicode{x2013}$cardinality estimates for semantic filters and joins are of high importance for optimizing query plans that include semantic operators. In this paper, we introduce SemCEB, the first benchmark for cardinality estimation over semantic operators, based on a real-world dataset of (semi-)structured text and images with 102 hand-curated, diverse queries spanning a wide range of selectivities, assessing cardinality estimation for semantic filters and joins in isolation. We evaluate sampling-based algorithms and Semantic Histograms, a state-of-the-art cardinality estimation algorithm for semantic operators, with respect to their accuracy, cost, latency, and memory overhead. We show that, while sampling is robust across different predicate categories, it does not scale and comes with high costs. Our adaptation of Semantic Histograms, on the other hand, is limited in its applicability, and its performance appears sensitive to the predicate category.

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arXiv2026

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

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 challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type. Indeed, there is no known mechanism for pruning non-qualifying row groups, e.g., when reading files from blob storage. In this work, we initiate the study of data skipping techniques for ML filters. We make the case that Parquet's default min-max metadata is enough to enable pruning. To this end, we draw connections to two lines of research: (i) the recently proposed query language for ML models and (ii) neural network verification. Our preliminary results on ReLU architectures show that on tables from TPC-H and TPC-DS, the average pruning effectiveness for filters of selectivity below 0.1% amounts to 27.4%. Finally, inspired by research on spatial joins, we propose 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 column pair. We observe an end-to-end speedup of 1.07$\times$ over PyTorch in DuckDB.

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arXiv2026

The Case for Cardinality Lower Bounds

Despite decades of research, cardinality estimation remains the optimizer's Achilles heel, with industrial-strength systems exhibiting a systemic tendency toward underestimation. At cloud scale, this is a severe production vulnerability: in Microsoft's Fabric Data Warehouse (DW), a mere 0.05% of extreme underestimates account for 95% of all CPU under-allocation, causing preventable slowdowns for thousands of queries daily. Yet recent theoretical work on provable upper bounds only corrects overestimation, leaving the more harmful problem of underestimation unaddressed. We argue that closing this gap is an urgent priority for the database community. As a vital step toward this goal, we introduce xBound, the first theoretical framework for computing provable join size lower bounds. By clipping the optimizer's estimates from below, xBound offers strict mathematical safety nets demanded by production systems - using only a handful of lightweight base table statistics. We demonstrate xBound's practical impact on Fabric DW: on the StackOverflow-CEB benchmark, it corrects 23.6% of Fabric DW's underestimates, yielding end-to-end query speedups of up to 20.1x, demonstrating that even a first step toward provable lower bounds can deliver meaningful production gains and motivating the community to further pursue this critical, open direction.

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arXiv2026

Mind the Gap. Doubling Constant Parametrization of Weighted Problems: TSP, Max-Cut, and More

Despite much research, hard weighted problems still resist super-polynomial improvements over their textbook solution. On the other hand, the unweighted versions of these problems have recently witnessed the sought-after speedups. Currently, the only way to repurpose the algorithm of the unweighted version for the weighted version is to employ a polynomial embedding of the input weights. This, however, introduces a pseudo-polynomial factor into the running time, which becomes impractical for arbitrarily weighted instances. In this paper, we introduce a new way to repurpose the algorithm of the unweighted problem. Specifically, we show that the time complexity of several well-known NP-hard problems operating over the $(\min, +)$ and $(\max, +)$ semirings, such as TSP, Weighted Max-Cut, and Edge-Weighted $k$-Clique, is proportional to that of their unweighted versions when the set of input weights has small doubling. We achieve this by a meta-algorithm that converts the input weights into polynomially bounded integers using the recent constructive Freiman's theorem by Randolph and Węgrzycki [ESA 2024] before applying the polynomial embedding.

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arXiv2025

Redbench: Workload Synthesis From Cloud Traces

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 query repetition and string-heavy queries. In this paper, we introduce Redbench, a novel benchmark featuring a workload generator that reproduces real-world workload characteristics derived from traces released by cloud providers. Redbench integrates multiple workload generation techniques to tailor workloads to specific objectives, transforming existing benchmarks into realistic query streams that preserve intrinsic workload characteristics. By focusing on inherent workload signals rather than execution-specific metrics, Redbench bridges the gap between synthetic and real workloads. Our evaluation shows that (1) Redbench produces more realistic and reproducible workloads for cloud data warehouse benchmarking, and (2) Redbench reveals the impact of system optimizations across four commercial data warehouse platforms. We believe that Redbench provides a crucial foundation for advancing research on optimization techniques for modern cloud data warehouses.

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arXiv2025

Instance-Optimized String Fingerprints

Recent research found that cloud data warehouses are text-heavy. However, their capabilities for efficiently processing string columns remain limited, relying primarily on techniques like dictionary encoding and prefix-based partition pruning. In recent work, we introduced string fingerprints - a lightweight secondary index structure designed to approximate LIKE predicates, albeit with false positives. This approach is particularly compelling for columnar query engines, where fingerprints can help reduce both compute and I/O overhead. We show that string fingerprints can be optimized for specific workloads using mixed-integer optimization, and that they can generalize to unseen table predicates. On an IMDb column evaluated in DuckDB v1.3, this yields table-scan speedups of up to 1.36$\times$.

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arXiv2025

Parachute: Single-Pass Bi-Directional Information Passing

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, sideways information passing enables only a uni-directional information flow, as opposed to instance-optimal algorithms, such as Yannakakis'. On the other hand, the latter require an additional pass over the input, which hinders adoption in production systems. In this paper, we make a step towards enabling single-pass bi-directional information passing during query execution. We achieve this by statically analyzing between which tables the information flow is blocked and by leveraging precomputed join-induced fingerprint columns on FK-tables. On the JOB benchmark, Parachute improves DuckDB v1.2's end-to-end execution time without and with semi-join filtering by 1.54x and 1.24x, respectively, when allowed to use 15% extra space.

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arXiv2025

Redbench: A Benchmark Reflecting Real Workloads

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 individually leveraged during the model training phase. However, there is a gap between research and industry that impedes the development of realistic learned components: the lack of suitable workloads. Existing ones, such as TPC-H and TPC-DS, and even more recent ones, such as DSB and CAB, fail to exhibit real workload patterns, particularly distribution shifts. In this paper, we introduce Redbench, a collection of 30 workloads that reflect query patterns observed in the real world. The workloads were obtained by sampling queries from support benchmarks and aligning them with workload characteristics observed in Redset.

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arXiv2024

Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification

Amplification by subsampling is one of the main primitives in machine learning with differential privacy (DP): Training a model on random batches instead of complete datasets results in stronger privacy. This is traditionally formalized via mechanism-agnostic subsampling guarantees that express the privacy parameters of a subsampled mechanism as a function of the original mechanism's privacy parameters. We propose the first general framework for deriving mechanism-specific guarantees, which leverage additional information beyond these parameters to more tightly characterize the subsampled mechanism's privacy. Such guarantees are of particular importance for privacy accounting, i.e., tracking privacy over multiple iterations. Overall, our framework based on conditional optimal transport lets us derive existing and novel guarantees for approximate DP, accounting with Rényi DP, and accounting with dominating pairs in a unified, principled manner. As an application, we analyze how subsampling affects the privacy of groups of multiple users. Our tight mechanism-specific bounds outperform tight mechanism-agnostic bounds and classic group privacy results.

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arXiv2024

Lightweight Correlation-Aware Table Compression

The growing adoption of data lakes for managing relational data necessitates efficient, open storage formats that provide high scan performance and competitive compression ratios. While existing formats achieve fast scans through lightweight encoding techniques, they have reached a plateau in terms of minimizing storage footprint. Recently, correlation-aware compression schemes have been shown to reduce file sizes further. Yet, current approaches either incur significant scan overheads or require manual specification of correlations, limiting their practicability. We present $\texttt{Virtual}$, a framework that integrates seamlessly with existing open formats to automatically leverage data correlations, achieving substantial compression gains while having minimal scan performance overhead. Experiments on data-gov datasets show that $\texttt{Virtual}$ reduces file sizes by up to 40% compared to Apache Parquet.

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arXiv2024

On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond

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 contraction ordering problem for tree tensor networks admits a polynomial-time algorithm, by drawing connections to database join ordering. The result relies on the adjacent sequence interchange property of the contraction cost, which enables a global decision of the contraction order based on local comparisons. Based on that, we specify a modified version of the IKKBZ database join ordering algorithm to find the optimal tree tensor network linear contraction order. Finally, we extend our algorithm as a heuristic to general contraction orders and arbitrary tensor network topologies.

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arXiv2024

DPconv: Super-Polynomially Faster Join Ordering

We revisit the join ordering problem in query optimization. The standard exact algorithm, DPccp, has a worst-case running time of $O(3^n)$. This is prohibitively expensive for large queries, which are not that uncommon anymore. We develop a new algorithmic framework based on subset convolution. DPconv achieves a super-polynomial speedup over DPccp, breaking the $O(3^n)$ time-barrier for the first time. We show that the instantiation of our framework for the $C_\max$ cost function is up to 30x faster than DPccp for large clique queries.

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arXiv2024

Approximate Min-Sum Subset Convolution

Exponential-time approximation has recently gained attention as a practical way to deal with the bitter NP-hardness of well-known optimization problems. We study for the first time the $(1 + \varepsilon)$-approximate min-sum subset convolution. This enables exponential-time $(1 + \varepsilon)$-approximation schemes for problems such as minimum-cost $k$-coloring, the prize-collecting Steiner tree, and many others in computational biology. Technically, we present both a weakly- and strongly-polynomial approximation algorithm for this convolution, running in time $\widetilde O(2^n \log M / \varepsilon)$ and $\widetilde O(2^\frac{3n}{2} / \sqrt{\varepsilon})$, respectively. Our work revives research on tropical subset convolutions after nearly two decades.

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arXiv2024

Corra: Correlation-Aware Column Compression

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 -- as data volume increases, it becomes more and more important to reduce storage cost on block storage (such as S3) as well as reduce memory pressure in multi-tenant in-memory buffers of cloud databases. However, single-column encoding schemes have reached a plateau in terms of the compression size they can achieve. We argue that this is due to the neglect of cross-column correlations. For instance, consider the column pair ($\texttt{city}$, $\texttt{zip_code}$). Typically, cities have only a few dozen unique zip codes. If this information is properly exploited, it can significantly reduce the space consumption of the latter column. In this work, we depart from the established path of compressing data using only single-column encoding schemes and introduce several what we call $\textit{horizontal}$, correlation-aware encoding schemes. We demonstrate their advantages over single-column encoding schemes on the well-known TPC-H's $\texttt{lineitem}$, LDBC's $\texttt{message}$, DMV, and Taxi datasets. Our correlation-aware encoding schemes save up to 58.3% of the compressed size over single-column schemes for $\texttt{lineitem}$'s $\texttt{receiptdate}$, 53.7% for DMV's $\texttt{zip_code}$, and 85.16% for Taxi's $\texttt{total_amount}$.

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arXiv2024

TSP Escapes the $O(2^n n^2)$ Curse

The dynamic programming solution to the traveling salesman problem due to Bellman, and independently Held and Karp, runs in time $O(2^n n^2)$, with no improvement in the last sixty years. We break this barrier for the first time by designing an algorithm that runs in deterministic time $2^n n^2 / 2^{Ω(\sqrt{\log n})}$. We achieve this by strategically remodeling the dynamic programming recursion as a min-plus matrix product, for which faster-than-naïve algorithms exist.

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arXiv2024

Did Fourier Really Meet Möbius? Fast Subset Convolution via FFT

In their seminal work on subset convolution, Björklund, Husfeldt, Kaski and Koivisto introduced the now well-known $O(2^n n^2)$-time evaluation of the subset convolution in the sum-product ring. This sparked a wave of remarkable results for fundamental problems, such as the minimum Steiner tree and the chromatic number. However, in spite of its theoretical improvement, large intermediate outputs and floating-point precision errors due to alternating addition and subtraction in its set function transforms make the algorithm unusable in practice. We provide a simple FFT-based algorithm that completely eliminates the need for set function transforms and maintains the running time of the original algorithm. This makes it possible to take advantage of nearly sixty years of research on efficient FFT implementations.

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