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Computer Science Databases

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Friday 2 October

Databases · Cross-listed from cs.HC

A study on human-agent teaming through spoken interaction: the impact of human individual traits and agent characteristics

Lara Gauder, Martin Bernardo Meza, Javier Krick et al. (+4 more)

Human-agent teams are collaborative systems where humans and agents work interdependently to achieve shared goals. The success of such teams is associated with the human's perception of the agent as a legitimate teammate. This perception is thought to depend not only on the agent's capabilities and reliability but also on social factors. We explore whether simple changes in an agent's communicative behavior can influence the human's perception of the agent's teammate-likeness. To this end, we developed a protocol based on a collaborative game requiring spoken interaction, in which a human subject and a virtual agent collaborate to identify a target object and place it on a board. Each subject interacted with two agents which provided the same task-relevant information but differed in behavior. While one was a neutral tool-like agent, the other was a team-building agent that used simple social strategies such as empathy, politeness, and positivity. Our analysis shows that the team-building agent was perceived as significantly more teammate-like, even in terms of its ability, despite both agents having identical capabilities for game-solving. Further, subjects with a higher propensity to trust automated systems and lower neuroticism tended to have a more favorable perception of the agent. Finally, we found that subjects tended to change their prosodic patterns when talking to the team-building agent, as a classifier based on prosodic features was able to predict the type of agent with better-than-random performance. A practically relevant conclusion is that minimal changes in spoken communication behavior of the agent, easily implemented in a variety of scenarios, can have a significant positive impact on the human's perception of its teammate-likeness.
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All authors Lara Gauder, Martin Bernardo Meza, Javier Krick, Alejandro Masin, Luciana Benotti, Marcos Luis Pietto, Luciana Ferrer

Databases

A Vision for a Logic-Based Workflow Formulation Framework and an Agentic Execution Pipeline for Pattern Engineering

Alankrit Moses, Raghav Mittal, Gautam Das et al. (+1 more)

Organizations increasingly depend on insights drawn from data to inform consequential decisions. Many concern structural behaviors, which we call patterns. Producing them may require working with multiple pattern types, performing different operations over them, and composing those operations into workflows, an activity we call pattern engineering. No principled framework exists for organizing such workflows: analysts devise each by hand, and existing systems automate particular analytical objectives rather than representing pattern operations in a general, composable form. We introduce a typed framework that represents each pattern through a fixed set of modular components, and expresses pattern operations as queries which specify input and output components. Chaining queries composes workflows, while execution of any individual query remains unconstrained. We instantiate the framework with an LLM-based agentic executor and evaluate example-mining, example-alignment, and pattern-induction queries over two temporal patterns, using World Intellectual Property Organization (WIPO) reports and patent data. All three queries are carried out across four LLM settings, which perform differently by target component, while the traces allow us to localize errors by execution stage.
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All authors Alankrit Moses, Raghav Mittal, Gautam Das, Chengkai Li

Databases

Conjunctive Queries with Negation: Beyond Signed-Acyclicity

Simon Frisk and Paraschos Koutris

We study constant-delay enumeration for conjunctive queries with negation ($\texttt{CQ}^{\neg}$). It is known that $\textit{signed-acyclic}$ queries can be enumerated with constant delay after linear preprocessing time, using linked lists with skip links. We use a hypergraph width measure for $\texttt{CQ}^{\neg}$, the $\textit{fractional signed hyperorder width}$ ($\textsf{sfhow}$), which requires a single variable order to simultaneously handle every subset of the negative atoms, which Capelli et al. introduced to characterize the preprocessing time of direct access. $\textsf{sfhow}$ strictly generalizes signed-acyclicity (recovered when $\textsf{sfhow} = 1$) and fractional hypertree width (recovered on queries without negation). Our main algorithmic result is a variable-elimination algorithm that achieves constant-delay enumeration on input $I$ for any full $\texttt{CQ}^{\neg}$ query with preprocessing time $O(|I|^{\textsf{sfhow}})$. Furthermore, we show that using multiple variable orders can improve this bound, exhibiting an $O(|I|^{3/2})$ algorithm for $k$-cycle queries with at least one negative edge.
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Databases · Cross-listed from cs.CL

FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs

Darian Lee, Shannon Rumsey, Jack St. Clair et al. (+3 more)

Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language onto schema entities and relations while handling the ambiguity inherent in how people phrase requests. Existing synthetic NL-to-SQL data generation methods largely ignore this ambiguity and produce oversimplified queries that fail to prepare models for the complexity of real-world structured knowledge access. We present FALCON, a framework that generates realistic, ambiguity-aware NL-to-SQL data matching the complexity of challenging real-world benchmarks, at low cost using compact open models. Our approach combines reserved-word SQL seeding and persona-based prompting to generate structurally complex queries, while alignment-based filtering preserves difficulty by distinguishing genuinely incorrect examples from complex but valid queries. Human evaluation confirms consistent high quality across model sizes, and our generated data exceeds existing benchmarks in both SQL complexity and natural language richness. Difficulty-stratified analysis shows models trained on FALCON data increasingly outperform baseline-trained models as query complexity increases, validating our pipeline's success in generating challenging training data. When combined with a small proportion of existing benchmark data, mixed training recovers performance on simpler queries while preserving these advantages on complex ones. The model- and database-agnostic design enables organizations to generate high-complexity NL-to-SQL training data locally without external APIs.
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All authors Darian Lee, Shannon Rumsey, Jack St. Clair, Xinyi Tang, Aditya Bansal, Yuanming Shi

Databases · Cross-listed from cs.AI

From Benchmarks to Production: A Text-to-SQL System for Complex Financial Data

Arijit Sehanobish, Bruno Gomes Coelho, Guillaume Michel et al. (+3 more)

General-purpose Text-to-SQL systems achieve strong performance on academic benchmarks like Spider and BIRD, where schemas are relatively shallow and column values are often human readable. In production financial databases, where concepts are stored as opaque integer keys rather than human-readable strings, these methods fall below 50%, as even simple queries require multiple joins and filter predicates reference opaque IDs. We present Financial LINking Text-to-SQL (FLINT), a domain-specialized Text-to-SQL system that closes this gap through three key components: (1) a lookup agent that dynamically resolves natural-language concepts to question-specific reference table constraints, (2) embedding-based retrieval of structurally similar query templates from a compact, expert-authored bank, and (3) schema linking that prunes a large table schema to the relevant subset by traversing foreign-key chains, rather than relying on name similarity alone. We evaluate on two datasets totaling 359 questions over production financial schemas. FLINT outperforms various state-of-the-art baselines using the same LLM. The system is deployed in production as part of a financial data retrieval service.
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All authors Arijit Sehanobish, Bruno Gomes Coelho, Guillaume Michel, Sophia Zhi, Valerie Faucon-Morin, Kristen Howell

Databases

TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series

Imane Hocine, Asma Abboura, Soror Sahri et al. (+3 more)

Streaming sensor applications routinely suffer from delayed or missing observations caused by faults, communication losses, or environmental interference. Although recent imputation methods exploit temporal and spatial dependencies effectively, most either assume offline access to future observations or prioritize throughput without enforcing domain plausibility. We present TSGuard, a real-time demonstration system for monitoring, validating, and imputing missing values in streaming time series. TSGuard combines a lightweight graph-aware temporal imputation model with constraint-aware validation, fallback estimation, and operator-facing explanations. Rather than treating imputation as an isolated prediction task, TSGuard integrates it into a broader data-quality loop: detect problematic observations, impute missing values, validate estimated against physical and spatial constraints, and either retain the original value as a plausible anomaly or replace it when it violates domain constraints. Using environmental sensing as a motivating setting, the demo enables users to inspect delayed sensors, compare imputers, define constraints, and validate flagged values in real time. The combination of lightweight online spatiotemporal imputation, domain-aware validation, and explicit retain-or-replace decisions is our central contribution, while interactive explanations make these decisions inspectable and actionable. for operators.
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All authors Imane Hocine, Asma Abboura, Soror Sahri, Abhijith Senthilkumar, Yacine Hakimi, Grégoire Danoy

Databases

Tracking State Footprints: How Agents Can Transact

Oto Mraz, Rares Şerban, Kyriakos Psarakis et al. (+2 more)

Can AI agents transact? We argue that they must: as multi-agent systems (MASs) increasingly write code, deploy infrastructure, modify databases, and call web services, lost updates or stale reads can be catastrophic. Although MASs increasingly execute plans in parallel, current orchestrators do not track the state that agents read and write. As a result, concurrency anomalies manifest even in simple coding tasks. We frame MAS coordination as a data management problem and propose to describe agents by their state footprint: the state they read and write across their own local context and state, as well as the state of the orchestrator and external systems. We posit that MASs require guarantees similar to those of databases, but providing them raises new challenges and opportunities: unlike database transactions, agents do not read from a fixed schema or an isolated snapshot, and cannot be replayed deterministically upon failure. They can, however, resolve conflicts semantically instead of aborting, enabling new forms of concurrency control and conflict resolution. Towards agents that can transact, we outline a vision for next-generation agent orchestrators and transactional interfaces for external systems to participate in agentic transactions.
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All authors Oto Mraz, Rares Şerban, Kyriakos Psarakis, Burcu Kulahcioglu Ozkan, Asterios Katsifodimos

Databases · Cross-listed from cs.LG

Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines

Maximilian Böther, Josh Wills, Ties Robroek et al. (+14 more)

Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caused by the parameter they changed rather than non-determinism in the training data sequence. The data loader must provide elastic determinism, i.e., a deterministic sequence of global training data batches despite changes to the GPU topology across runs (e.g., due to GPU scarcity), frequent checkpoint-resume cycles, and different data processing execution backends. Achieving this is difficult because modern foundation model data pipelines tokenize, pack, and mix samples online, introducing stateful n-to-m transformations that break sample indexing. Existing data loaders largely assume indexable 1-to-1 pipelines, and the common workaround of offline materialization is expensive and, for some modalities such as video, infeasible. We present Zephon, a data loader for foundation models that supports online, stateful pipelines while providing elastic determinism and efficient resumption from checkpoints. It partitions the global stream into topology-independent lanes, serializes ordering decisions while parallelizing stateless work on interchangeable backends, and checkpoints only bounded in-flight state so recovery cost does not grow with training progress. We evaluate Zephon on text and vision-language workloads and show that it achieves competitive throughput while providing a combination of guarantees that no existing loader offers for online, stateful pipelines.
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All authors Maximilian Böther, Josh Wills, Ties Robroek, Sonnet Xu, Paul Burstein, Daniel Zayas, Cody Blakeney, Siddharth Joshi, Haoli Yin, Rishabh Adiga, Haakon Mongstad, Luke Merrick, Pratyush Maini, Ari Morcos, Matthew Leavitt, Ana Klimovic, Bogdan Gaza

Databases · Cross-listed from q-fin.TR

Axient: Manifest-Bound Evidence for On-Chain Financial Protocols: Seven-Layer Derivation, Correlation, Tamper Rejection, and Reproducible Claim Promotion

Maksym Nechepurenko

Hybrid on-chain financial protocols are frequently evaluated with evidence that is individually useful but collectively insufficient: a unit test, transaction receipt, screenshot, service response, or several matching hashes may be presented as proof of a complete workflow even when the layers share one generated source or omit the financial authority that matters. This paper develops a manifest-bound evidence architecture for such systems. A sealed execution manifest binds declared system identities, roles, schemas, and release identity. Seven evidence layers are retained with registered derivations and linked by a common correlation identity. A conjunctive claim-promotion rule requires provenance, predicate-specific root conditions, negative controls, reproducible packaging, historical replay, and independent review in addition to hash equality. We formalize the evidence graph, prove that copied hashes cannot establish independent derivation, show how collision-resistant commitments make specified tampering detectable, define exactly-once financial-effect evidence, and distinguish original historical identities from later reproducibility attestations. The registered local case study records twelve parent Financial Interaction Assertions in one accepted cohort: 84 retained ACTUAL_EVIDENCE positions, two clean materializations with one byte-identical file index, a deterministic strict archive accepted by a separate verifier, and a selected ordered replay bound to that archive. FIA-011 is conjunctive and retains both stale-before-admission and book-epoch-change-mid-position subcases. The result demonstrates a reviewable chain of custody from financial assertion to replay-bound package while preserving the difference between observed evidence, interpretation, and future hypotheses.
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Databases · Cross-listed from cs.CL

AptMQL-Bench: From Text-to-SQL to Text-to-MQL via Access-Pattern Schema Design and Data-Preserving Migration

Hy Nguyen, Nabi Rezvani and Robin Vujanic

Document databases such as MongoDB are core infrastructure for modern applications, and natural-language interfaces to them—text-to-MQL—would let non-experts query complex, semi-structured data without mastering the query language. Progress on this task depends on high-quality benchmarks, which are most practically obtained by converting an existing text-to-SQL benchmark to the document setting. Unfortunately, existing efforts rely on heuristics for mechanical conversion: the document schema mirrors the relational foreign-key graph, and each query mirrors its source SQL. As a result in our experiments, these approaches fail to migrate 6 of 21 BIRD databases outright, silently drop up to 25.9% of rows on others, and yield schemas whose ground-truth queries run over an order of magnitude slower as the data scales. We instead propose a conversion pipeline, driven by coding agents with human-in-the-loop verification, that designs each document schema from expected access patterns and rewrites queries to be MongoDB-native. Applying it to BIRD, we build an access-pattern-based text-to-MQL benchmark (AptMQL-Bench). It includes 21 document-oriented databases, 3,186 natural-language requests, and their associated MQL queries—whose databases are migrated from SQLite without data loss and scale efficiently. The strongest model, Claude Opus 4.5, achieves only 57.38% accuracy without external knowledge evidence and 70.34% with it. This indicates that realistic text-to-MQL generation remains challenging.
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Databases · Cross-listed from cs.OS

FDP: The Data Placement Promise of Modern NVMe SSDs

Sijie Lan, Hui Qi, Xing He et al. (+3 more)

NVMe SSDs are now widely deployed as the storage tier in data centers. As SSDs have evolved over the past decade, the commu- nity has continued to debate the interfaces they expose and how operating systems and storage systems should exploit them. The NVMe Flexible Data Placement (FDP) proposal is the latest point in this design space. FDP introduces an interface based on Reclaim Units that enables explicit data placement to reduce device write amplification without the software engineering costs of sequential- write constraints and host garbage collection. FDP-enabled SSDs are emerging in commercial products and early data center de- ployments. Their compatibility with conventional block I/O allows existing applications to run unchanged, allowing a frictionless adop- tion in industry. This paper presents an experimental evaluation of FDP SSDs to characterize their data placement guarantees over the raw device interface. We then revisit two widely deployed and distinct open- source storage systems, MySQL and RocksDB, and examine whether lifetime-based data separation and distinct write patterns built into their architectures can be mapped onto FDP SSDs without inva- sive changes. Our evaluation shows end-to-end WAF reductions at higher device utilization, along with QoS and throughput improve- ments under synthetic and real-world workloads. These results demonstrate that FDP provides a practical and deployable cross- layer mechanism for data placement with open-source ecosystem support on Linux. They also highlight why FDP SSDs are gaining traction in industry.
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All authors Sijie Lan, Hui Qi, Xing He, Mahmut Kandemir, Javier González, Vivek Shah

Databases

RaBitQ-SSD: Split Codes and Pipelined I/O for SSD-Resident Vector Search

Yuexuan Xu, Jianyang Gao, Michael Norris et al. (+5 more)

SSD-resident approximate nearest-neighbor search is essential when vector collections exceed DRAM capacity. The challenge is to reduce SSD reads and hide I/O latency through concurrent reads and overlap with computation. However, for graph-based search, progressive candidate discovery limits advance I/O planning while IVF search reads inverted lists in full, incurring unnecessary SSD reads. In this paper, we present RaBitQ-SSD, an extension of IVF-RaBitQ for SSD-resident vector search. Specifically, we propose Split-RaBitQ, which retains a configurable prefix of each binary code in DRAM, allowing in-memory code storage below one bit per dimension. Using this partial representation, it provides an unbiased distance estimator with a probabilistic error bound. Once the in-memory coarse quantizer identifies candidate lists, this bound supports pruning individual candidates within them, enabling finer-grained SSD access. We also design an asynchronous search pipeline that coordinates candidate pruning with SSD read scheduling to reduce unnecessary reads while overlapping I/O with computation. On datasets ranging from $5$ million to $1$ billion vectors, RaBitQ-SSD delivers up to $1.74\times$ the throughput of graph-based baselines at $90\%$ recall, while reducing SSD page reads by up to $3.8\times$. Its on-SSD indexes are up to $7.0\times$ smaller and $10.0\times$ faster to build than DiskANN. We also build and search an index of $10$ billion vectors on a single machine with one SSD, achieving over $3{,}000$ queries per second at $90\%$ recall.
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All authors Yuexuan Xu, Jianyang Gao, Michael Norris, Alibek Zhakubayev, Pankaj Singh, Junjie Qi, Matthijs Douze, Cheng Long

Databases

Query Performance Tuning with Optimal Exploration of Optimizer Cost Model Parameter Space

Wentao Wu, Xiaoying Wang, Vivek Narasayya et al. (+1 more)

Modern query optimizers use analytical cost models to estimate the cost of a given query plan. Such cost models are typically functions of a set of "cost units" that specify unit CPU cost when processing a row or unit IO cost when accessing a disk page. These cost units are traditionally viewed as platform-dependent constants, that is, they require a one-shot calibration when a database is deployed on a hardware/software platform, but are fixed afterward regardless of the query being optimized for. Some very recent work has taken a different perspective by viewing these cost units as tunable parameters that we call "cost model parameters (CMPs)" in this paper. However, so far there is no approach that offers any optimality guarantee for the tuning results. We present a new approach to systematically explore the query plan space spanned by the CMPs and find the best plan in terms of execution time. Compared to alternative exploration approaches that use random search (RS) or Bayesian optimization (BO), our new approach is deterministic and, therefore, avoids the undesirable instability that is inevitable when applying RS or BO. Moreover, it is guaranteed to find all candidate plans in the query plan space without suffering from the overhead of an exhaustive enumeration. We also present a set of optimization techniques to reduce the overall evaluation time spent on executing the candidate plans found, a factor that is often overlooked by previous work but is critical from a practical point of view. Experimental evaluation on top of PostgreSQL and Microsoft SQL Server demonstrates the efficacy of tuning the CMPs, which can find query plans that are orders of magnitude faster in execution time than the ones found by RS or BO.
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All authors Wentao Wu, Xiaoying Wang, Vivek Narasayya, Surajit Chaudhuri

Thursday 1 October

Databases

Look Here or Look Across: Unified Cardinality Constraints for N-ary Relationships

Huanyi Chen

A cardinality constraint written on an edge of an entity-relationship diagram admits two opposite readings. Under the reading used by UML and by Chen's original model, the label is read with the entity set on the same side of the relationship; under the reading used by standard database textbooks, it is read with the entity set on the opposite side. The two readings are exact opposites, so a reader who assumes the wrong one takes away the opposite of what the designer meant. For binary relationships the difficulty is confined to interpretation, because the two constraints a binary relationship set admits can both be drawn. For relationships among three or more entity sets the picture is worse: a ternary relationship set admits twelve cardinality constraints, and a diagram with three edges can carry at most three of them. This paper presents a notation, Card(R; p; q) = (lower, upper), that removes the ambiguity without taking a side in it, and that is not limited to one constraint per edge. We give the number of constraints an n-ary relationship set admits, show which of them a design determines without ever writing them down, and state two inference rules, decomposition and augmentation, that derive one constraint from another, together with the side conditions under which each is sound. We close with a worked case study that turns three business requirements into three explicit constraints and nine more that the design decides on its own.
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Databases

TREMOR: Template Matching for Large Seismic Data Collections

Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire et al. (+3 more)

Seismic station networks continuously record the ground velocity at several locations on earth in the form of waveform data series, which seismologists analyze to detect various kinds of geophysical events, including earthquakes. Some of these events are of particular interest: they are called templates and are used to search the seismic data collections for matching, similar events. This is known as template matching, and is a fundamental task in seismology, serving as the backbone for various seismic analyses. However, template matching requires extensive processing times, especially for seismic collections that exceed the memory capacity of a single machine. This poses a significant challenge to seismologists and is becoming worse as the seismological datasets continue to grow in size. In this paper, we introduce TREMOR, a distributed data series processing framework for template matching, designed to efficiently handle large waveform collections. We apply TREMOR to two representative real-world seismic use cases for template matching and, through an extensive experimental evaluation, we demonstrate its efficiency, with TREMOR being up to 12x faster than the best competing method, while returning the same, exact results.
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All authors Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire, Yoann Cano, Léonard Seydoux, Themis Palpanas

Databases

SOLO: Certified-Recall Metric Similarity Search with Scan-Only Sampled Inverted Lists

Édgar Chávez

In every fast nearest-neighbor index, recall is measured, never predicted: each operating point is tuned by serving it against ground truth. SOLO is an index whose recall is computed from the index itself, before any query is served. SOLO is an inverted file whose vocabulary is a random sample of the database. Each object's $k_b$ nearest sample points are stored once, ranked; at serve time an object is posted under the first $b \le k_b$ of them, and a query scans the lists of its $k_s$ nearest sample points exhaustively, with the true distance. Recall depends on the product $b \cdot k_s$ (an equal-work law), so the search-side $k_s$ compensates for a small $b$ with no rebuild. There is no beam, vote or pruning bound, so a true neighbor is missed only if it shares no sample point with the query – a membership event decided by stored integers, not by a search. Recall is therefore a count: one ground-truth pass over a sample of the operator's queries certifies every $(b, k_s)$ at once, with nothing served. The certificate matches served recall to four decimals from $10^6$ to $10^9$ objects, including 768-dimensional text embeddings under inner product with shifted queries. No graph index has an analogous object. The rest is the same rule applied recursively: a list that outgrows a bound is sampled and split like the database, and so is the vocabulary itself, which is what drives resident memory down. Deep-100M is served at recall 0.9977 from 1 GB of enforced resident memory, Deep-1B at 0.9925 from 96 MB. Inserts are one search, deletes are exact, and throughput reaches $1.8\times$ a tuned HNSW at $10^8$. Every number is reported against HNSW, DiskANN, GRAFT, NAPP, misi, SPANN, ScaNN and RaBitQ on the same hardware and ground truth.
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Databases · Cross-listed from cs.LG

ArrivalBench: Agent-Generated Data Pipelines Are Correct Once and Wrong Under Time

Pranay Kothari

Benchmarks for agent-generated data work grade a pipeline by running it once against a fixed snapshot. ArrivalBench instead re-executes the pipeline an agent leaves behind under adversarial but replayable delivery schedules (late, duplicated, out-of-order and retried records) and requires its final state to equal a batch recomputation of the complete log. Because the oracle recomputes rather than classifies, a wrong table and a crash are distinct verdicts: a crash is visible to monitoring a team already runs, and a wrong table is not. On 40 tasks we built, our reimplementation of single-execution grading certifies 86-100% of the pipelines eleven models produce; re-executing the same artifacts finds 7.0-79.2% of the certified ones silently wrong. The gap is not produced by the repair loop: within the same model and task, pipelines repaired against the snapshot test fail replay about as often as those that passed it first time. In every model, idempotency hazards fail more often than ordering hazards. Separating a wrong answer from a crash also changes how interventions read: a hazard warning cuts one model's silent failure from 48.2% to 10.5% while raising its crash rate from 9.0% to 37.0%, so all-in failure moves only from 51.0% to 44.0%. All eleven arms were independently re-run, and rates moved by at most 5.9 points.
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Databases · Cross-listed from cs.CL

Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing et al. (+2 more)

Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real enterprise warehouses are too sensitive to release, these benchmarks are built on public datasets where a business event fits in a single table. We introduce Argo-Bench, an evaluation framework comprising 210 data science and analytics tasks. Drawing on public data, peer-reviewed industry literature, and regulatory filings, we simulate a food delivery platform in New York City at true scale, with 81 million orders in 2024, grounded economics, fraud patterns, and marketplace incentives. We export this world to an ERP warehouse of 235 tables and 7.5 billion rows, modeled on the Oracle E-Business Suite schema. The simulator's ground-truth state is withheld from the warehouse the agent sees, so tasks require reconstructing facts by navigating the warehouse before acting on them. Argo-Bench goes beyond text-to-SQL: the agent files actions such as banning fraudulent accounts, allocating courier incentive budgets, or issuing back pay, and the grader scores each by its consequences in the simulator. Every task has an executable reference solution that demonstrates solvability using only the warehouse. The strongest of 14 frontier and open-weight models scores 95 or higher on only 34.8% of tasks and averages 59.5 points. We hope Argo-Bench drives progress toward agents that understand, navigate, and act within real data environments.
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All authors Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing, Duke Gand, Joseph J Ma

Databases

Prune First, Decide Fast: Scalable Semantic Query Processing with JEVDB

Zhengle Wang, Hanxu Yan, Fuheng Zhao et al. (+1 more)

Semantic database systems extend SQL with foundation-model inference over unstructured data, but current engines rely heavily on autoregressive LLMs for discrete relational decisions, creating high latency and monetary cost. We present JEVDB, a scalable semantic database system that uses fast, typed decision models for semantic filters, joins, classification, and ranking, while selectively escalating uncertain cases to generative LLMs. To reduce semantic-join work, JEVDB combines exact Yannakakis-style semijoin reduction over relational structure with Semantic Bloom Filters (SBFs), which use registered necessary conditions to screen candidates across latent semantic edges. We evaluate JEVDB on SemBench and Shelob, a TPC-DS-derived semantic-join workload. On SemBench, JEVDB-Flash achieves the lowest latency on all 21 evaluated queries and the lowest cost on 19, while maintaining competitive answer quality. On Shelob, where joins scale to 540K candidate pairs, JEVDB completes all queries with 95.7%-97.5% mean F1. SBF screening removes 87.4% of candidate pairs before semantic evaluation, and reusable condition-index scoring further reduces reasoning-model escalations by 55.2%. An interactive query simulator, source code, and benchmarks are available at https://jevdb.org.
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All authors Zhengle Wang, Hanxu Yan, Fuheng Zhao, Chunwei Liu

Databases

DIADA: Automatic Data Composition in Data Lakes

Marc Maynou, Albert Martin, Sergi Nadal et al. (+2 more)

Data lakes contain a plethora of attributes scattered across many tables that, when combined, provide enhanced assets for data analysis. Nonetheless, deciding which attributes belong together in meaningful relations remains a manual, per-task effort. Merging by joinability alone provides no guarantees regarding attribute relevance, while selecting features against a single target discards attributes useful to other tasks. To address this gap, we introduce the data composition problem: organizing a fragmented, heterogeneous lake into meaningful relations, agnostic of any particular analytical task so that the resulting organization can serve as a common foundation for diverse downstream analyses. We propose DIADA, a composition system that employs multivariate dependence as the criterion for assessing the meaningfulness of a relation and approximates it by hypothesizing independence among attributes and identifying those sets that violate this hypothesis. To do so, we map the attributes to a predicate space, forming a lattice under inclusion and mining those predicate sets that exhibit dependence among their constituents. We contribute a dedicated and scalable algorithm to effectively explore this space, outscaling classical algorithms for mining relationships, thus discovering dependencies that would otherwise be impractical to identify. We demonstrate that applying a single data composition process benefits diverse potential downstream tasks. This is the result of providing a subset of low-noise, statistically relevant attributes that increases the confidence that detected patterns are grounded in real relationships, thus preventing common modeling issues in large-scale environments.
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All authors Marc Maynou, Albert Martin, Sergi Nadal, Anna Queralt, Oscar Romero

Databases · Cross-listed from cs.CL

Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?

Laura van Weesep, Riccardo Tedoldi, Jens Sjölund et al. (+6 more)

The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repositories and industrial screening databases suffer from missing, inconsistent, or conflated assay annotations. In this work, we quantify the extent of missing annotations in PubChem for the BioAssay Ontology (BAO) assay format and physical detection method fields and investigate whether open-source and proprietary large language models (LLMs) can reliably predict and audit metadata annotations directly from the assay text. In our assessment, we found that the annotation coverage across PubChem's $\sim$2 million bioassays is critically sparse, 36% lacking an assay format, 89% a BioAssay type, and >99.9% any BAO-mapped assay format or detection technology term. This motivates the need for automated test-metadata curation. Using evaluation sets derived from PubChem and ChEMBL, we assess the agreement of seven open-source and proprietary LLMs with existing silver labels. Recall is at least 0.96 for biochemical and cell-based assay formats, with a similar pattern for detection technology, although disagreements increase on under-represented classes. Manual inspection shows that many of these disagreements trace back to inconsistencies between silver sources rather than to LLM error. Moreover, in a qualitative study with a senior industrial curator, LLM-generated evidence prompted the expert to revise some of their own labels, showing LLMs can flag potentially mislabeled assays. Across the study, performance differences between proprietary and open-source models were small. Together, these results suggest LLMs can support the large-scale annotation and auditing of assay metadata, though per-class reliability estimates and targeted human review remain necessary before such labels enter downstream ML pipelines.
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All authors Laura van Weesep, Riccardo Tedoldi, Jens Sjölund, Hossein Azizpour, Susanne Winiwarter, Ola Engkvist, Jon Paul Janet, Samuel Genheden, Juan Viguera Diez

Databases

Bridging the Omics Divide: A Modular Relational Approach to Multi-Layer Biological Data Management

Alessandro Balestrucci, Donald Friggieri, Andrea Gariboldi et al. (+1 more)

Background: Rapid growth of high-throughput molecular data demands systems for efficient retrieval, integration, and scalability across omics layers. Traditional file-based workflows hinder cross-modal analysis and reproducibility because of fragmented storage and ad-hoc querying. Few existing tools for genomic variation data prioritize modular multi-omics integration. We developed vcf2db, a sample-centric relational framework modeling each omics modality as a distinct but linkable component centered on biological samples. This study evaluates whether this design delivers competitive genomic retrieval while enabling extension to additional molecular layers. Results: We implemented a proof-of-concept genomic schema and ingestion pipeline for annotated VCF data using the European subset of the 1000 Genomes Project (502 samples, 25 million variants). We benchmarked it against three established VCF-oriented tools on seven retrieval tasks: coordinate filtering, annotation-driven queries, genotype extraction, and aggregation. Under controlled conditions, vcf2db performed strongly on selective queries, often outperforming other systems for coordinate and annotation filters, and remained usable for genotype retrieval. Aggregation-heavy tasks were less efficient, indicating optimization targets. We also validated modular extensibility by adding a synthetic transcriptomic layer without modifying genomic tables, linking layers via shared sample identifiers. Conclusion: vcf2db supports cross-layer retrieval directly as SQL queries anchored on shared sample identifiers, enabling integrated multi-omics access that is difficult with file-based approaches.
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All authors Alessandro Balestrucci, Donald Friggieri, Andrea Gariboldi, Panagiotis Alexiou

Databases · Cross-listed from q-bio.NC

A High-Density EEG Dataset for Stimulus-Driven Auditory Attention

Ruofan Yan, Na Lu, Shu Peng et al. (+6 more)

Stimulus-driven auditory attention determines which sound gains priority when multiple sources compete without an explicit listening goal, yet most computational studies focus either on acoustic salience or on decoding predefined attended targets. This study investigates instruction-free auditory competition using the Stimulus-driven Auditory Attention (SAAD) paradigm and develops a neurophysiologically informed framework that integrates stimulus-derived sound priority with trial-specific EEG evidence. Behavioral analysis using a Bradley–Terry model showed that sound priority estimated from previous competitions generalized to unseen sound pairings, improving held-out prediction from an AUC of 0.577 to 0.718. EEG analysis further revealed mid-to-late centro-temporal lateralization associated with the reported selection side, with neural information remaining predictive beyond acoustic asymmetry. Guided by these findings, the proposed model first estimates a latent priority for each competing sound and forms relative stimulus evidence from their difference. A multi-scale EEG pathway with complementary signed and power-based readouts then extracts trial-specific neural evidence, which is incorporated through gated decision-level integration. The framework is evaluated using mirror-constrained and pairing-held-out protocols, together with representative acoustic, EEG, multimodal baselines, and systematic ablations. The results support a computational account in which spontaneous auditory selection reflects the interaction between generalizable stimulus priority and trial-specific neural variability.
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All authors Ruofan Yan, Na Lu, Shu Peng, Wenlong You, Zhige Chen, Yuxuan Yan, Yan Liu, Kay Chen Tan, Jibin Wu

Databases · Cross-listed from cs.CY

WIP: DBWorkout: A Gamified SQL Practice Platform to Support Formative Learning in Database Courses

Sehrish Basir Nizamani, Deepika Devaraj, Tien Nguyen et al. (+4 more)

This research WIP paper presents DBWorkout, a web-based platform that supports formative SQL learning through sandbox-based execution, automated result-based feedback, and session-based gamification. Learning Structured Query Language (SQL) remains challenging for undergraduate students due to limited opportunities for interactive practice and immediate feedback. Students iteratively practice SQL on live database instances while receiving multi-dimensional feedback on query correctness, including row values, column structure, and ordering. To reduce instructor workload, DBWorkout incorporates large language model (LLM)-assisted tools for schema and task generation within a human-in-the-loop workflow. A pilot study with teaching assistants and a classroom deployment involving 170 undergraduate students across two in-class sessions show strong perceived learning value (90% agreement) and engagement (87% enjoyment), alongside low reported pressure (22%). However, only 42% of students found the automated feedback sufficiently actionable, a finding independently corroborated by 40% of open-ended responses raising feedback quality concerns, providing cross-method triangulation of this gap. These findings demonstrate the technical feasibility and early pedagogical potential of DBWorkout while identifying directions for enhancing feedback quality and supporting sustained SQL learning.
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All authors Sehrish Basir Nizamani, Deepika Devaraj, Tien Nguyen, Khyati Goyal, Saad Nizamani, Sally Hamouda, Jaren Goldberg

Databases · Cross-listed from cs.LG

Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

Roberto Stanzione, Jules Barbe, Magali Parrino et al. (+2 more)

Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to the development of numerous detection methods, as well as a variety of benchmarks aimed at thoroughly evaluating their performance. However, most existing detectors remain largely agnostic to domain context, overlooking explainability and interpretability. One of the main reasons for this gap is that current benchmarks primarily focus on detection accuracy, and only few of them evaluate spatial explainability. Moreover, no benchmark currently provides sufficiently rich semantic annotations to support the generation of human-understandable interpretations of anomalies. To address these limitations, we introduce SHAD (Scality High-dimensional Anomaly Detection benchmark), a fully annotated benchmark composed of 215 multivariate, high-dimensional time series collected from real-world distributed cloud storage systems operated by Scality. The proposed dataset includes rich contextual information, covering three families of anomalies with varying degrees of severity. As further contribution, we provide a foundation for future work by evaluating baseline methods for Detection, Explainability, and Interpretability, covering all stages of a TSAD pipeline. For Detection, we benchmark a wide range of existing anomaly detectors, testing their effectiveness on the proposed real-world dataset. Then, we consider explainability by evaluating whether measuring the contribution of each dimension in the generated anomaly score can provide accurate anomaly attributions. Finally, for interpretability, we investigate the effectiveness of frozen LLM baselines in localizing and interpreting anomalies.
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All authors Roberto Stanzione, Jules Barbe, Magali Parrino, Jérémie Fourmann, Paul Boniol

Databases · Cross-listed from cs.CL

JoinGR: Learning to Traverse Join Graphs for Table Retrieval

Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay et al. (+1 more)

Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key source of evidence: some required tables are not mentioned in the question and become identifiable only through their join relationships to already relevant tables. We introduce JOINGR, a join-aware table retrieval method that treats the database join graph as the retrieval space. Columns are represented as graph nodes, while intra-table and foreign-key relationships are represented as typed edges. Given a question, JOINGR selects semantically similar anchor tables, traverses join edges with a query-conditioned scorer, and aggregates the resulting edge deposits into table scores. The scorer is a lightweight MLP on top of frozen query, node, and edge embeddings, trained with a pairwise margin loss over gold tables. On BIRD and Spider datasets, JOINGR is competitive with the strongest retrieval baselines. On BEAVER, a challenging enterprise benchmark with multi-hop table requirements, JOINGR substantially improves recall over dense retrieval and re-ranking baselines. Cross-domain experiments show that the learned scorer transfers across benchmarks, indicating that the method captures reusable joingraph traversal behavior.
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All authors Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay, Vivek Gupta

Databases

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

Abdalla Mohamed and Ashraf Aboulnaga

Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to previously unseen databases and tasks. To make a prediction for a target row, an RFM samples a neighborhood of rows linked to that row through foreign keys and uses this neighborhood as its inference context. Lowering inference cost is an important goal for any foundation model, and for RFMs this cost grows with the size of the context. The simplest ways to shrink the context is to drop some of the sampled rows, but this ignores the semantics of the database schema, so it is as likely to discard informative rows as uninformative ones. We propose STEER, a sampling approach that shrinks the inference context by concentrating it on the tables most relevant to the prediction task at hand. STEER obtains relevance information by prompting a large language model to rank the foreign-key edges of the database schema into relevance tiers for the given task, and then maps each tier to a probability of following that edge during traversal. Because the ranking uses only the schema, it is computed once per task and reused across all subsequent predictions, amortizing its cost. We evaluate STEER on three state-of-the-art RFMs (RT, RT-J, and Griffin) and show that it reduces inference context size by about 40% on average while maintaining, and in some cases improving, prediction accuracy.
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Databases

HakiCC: LLM-Driven Multi-Agent Design and Optimization of Concurrency Control Protocols

Farzad Habibi, Juncheng Fang and Faisal Nawab

Large language models (LLMs) have recently been applied in systems research as a tool to reduce human-intensive engineering effort through cost-efficient automation. Decades of research have produced a rich landscape of concurrency control (CC) protocols, each encoding distinct trade-offs in correctness, throughput, and abort behavior. However, most applications in practice default to 2PL or OCC, because selecting and adapting a protocol to a specific application requires expert knowledge that is rarely available to application designers. This is a wasted opportunity, as an application-specific CC protocol can yield significant performance advantages over a generic baseline, but designing one requires deep expertise in CC protocol design. In this paper, we propose HakiCC, an LLM-driven multi-agent pipeline that automatically designs, verifies, and optimizes concurrency control protocols tailored to a given target application. HakiCC provides a two-stage pipeline. In Stage 1, a multi-agent system takes a workload description as input and generates an application-specific CC protocol implementation, which is iteratively repaired and verified for conflict-serializability. In Stage 2, the verified protocol is further optimized for that application through an LLM-driven evolutionary loop targeting correctness and throughput. We evaluate HakiCC on TPC-C and AuctionMark as target workloads, producing and reporting ten application-specific CC protocols. All ten are conflict-serializable after Stage 1; Stage 2 improves throughput for every protocol, with average gains of +50.6% for TPC-C protocols and +92.2% for AuctionMark protocols.
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Wednesday 30 September

Databases · Cross-listed from cs.DC

Feature Freshness Budgets for Real-Time ML Inference Under Stream Lag

Amit Rajula

Online feature stores decouple feature computation from model serving, materializing features from upstream event streams into a low-latency store that inference reads at request time. This decoupling introduces a freshness gap - the interval between an event occurring at the source and its effect becoming visible in the served feature vector - that is widely documented as a cause of training-serving skew but has no formal treatment as a bounded, cost-quantified quantity. We model the online feature store as a materialized view over an event stream and define a per-feature freshness budget: the difference between the decision window a feature is consumed within and the staleness the serving pipeline imposes on it. We prove two propositions - a per-feature staleness bound, and a closed-form threshold below which a feature can never be served within budget - and validate them in a seeded, reproducible discrete-event simulation and on a real Apache Kafka, Redis, and PostgreSQL pipeline. The predicted collapse threshold reproduces on real infrastructure, with the decision-error transition landing where the closed form predicts. Reconciling simulated and real drop rates surfaces a methodological result of independent interest: the dominant simulation-to-infrastructure gap is not latency but a sampling-phase artifact - a simulation whose request clock and recomputation cadence are phase-locked systematically under-observes staleness. Randomizing that phase reconciles simulation and infrastructure to within 0.018 mean absolute error in per-feature drop rate. Results are scoped to the studied workloads and configurations and are not claims about any specific production feature-store implementation.
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Databases

TabJoinBench: A Benchmark for Joinable Table Discovery

Sandipan De, Jin Wang and Vivek Gupta

Join discovery aims to identify tables from large data repositories that can augment a query table with complementary information, enabling downstream tasks such as data exploration, feature engineering, and business intelligence. Although numerous join discovery methods have been proposed, existing studies rely on method-specific benchmark construction, making reproducible and fair comparison difficult. We present TabJoinBench, a benchmark for evaluating join discovery methods across semantic, relational, and hybrid data lake scenarios. TabJoinBench constructs query-candidate pairs using source-specific validation strategies, systematically introduces structural, representation, and semantic changes through composable perturbations while preserving reliable ground truth. We evaluate representative join discovery methods spanning set-based, feature-based, and learned approaches, together with general-purpose language-model embedding baselines, and publicly release the processed datasets, ground-truth annotations, and generation pipeline to facilitate reproducible evaluation and future research.
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