84 papers
This paper introduces NeurCC, a novel learned concurrency control algorithm that utilizes Bayesian optimization and a graph reduction search to efficiently learn a high-performance function mapping database states to control actions, thereby consistently outperforming state-of-the-art algorithms across diverse and dynamic workloads.
This paper demonstrates that redesigning database systems to adopt out-of-place writes, exemplified by a modified LeanStore, significantly reduces write amplification and boosts throughput across diverse OLTP benchmarks while enabling support for modern SSD interfaces like ZNS and FDP.
This paper presents the first systematic study of the expressive power of the PG-Keys language by establishing a unifying framework to compare it with Graph Functional Dependencies (GFD) and Graph Generating Dependencies (GGD), ultimately revealing a strict hierarchy of expressiveness that clarifies PG-Keys' capabilities within the context of the upcoming GQL standard.
This paper introduces a Neuro-Symbolic Generative Agent that overcomes the "Implicit Context" problem in scientific discovery by autonomously validating and completing physical mechanisms through dimensionless scaling analysis, thereby preventing physical hallucinations and ensuring thermodynamically consistent simulations.
This paper introduces the concept of "local stability" to assess how minor changes in item values affect their rankings, particularly within dense regions of similar-quality items, and proposes efficient sampling-based algorithms (LStability and Detect-Dense-Region) with theoretical guarantees to compute this measure and identify such regions.
This paper advances the resolution of the bounded derivation depth implies finite controllability (BDD FC) conjecture by proving that universal models generated by BDD rule sets cannot contain arbitrarily large tournaments without entailing a loop query, thereby significantly narrowing the space of potential counterexamples.
This paper introduces GeoBenchr, an open-source, application-centric benchmarking suite designed to comprehensively evaluate and compare the performance, scalability, and configuration impact of spatiotemporal database platforms across diverse real-world use cases.
This ICDE 2026 tutorial paper provides a comprehensive overview of the synergistic "virtuous cycle" between AI and vector search, detailing how AI enhances vector search efficiency and how vector search, particularly through Retrieval-Augmented Generation, empowers Large Language Models, while also exploring co-optimization strategies, challenges, and future research directions.
This paper evaluates the practical effectiveness of LLM-driven index tuning against Microsoft's Database Tuning Advisor (DTA) using industrial and real-world workloads, finding that while LLMs can identify superior configurations and capture human-intuitive insights, their substantial performance variance and high validation costs currently limit their direct adoption in production as a standalone replacement for DTA.
Nezha is a distributed key-value store that resolves the I/O overhead caused by overlapping persistence operations in Raft-based systems by integrating key-value separation with an optimized persistence strategy and leveled garbage collection, thereby achieving significant throughput improvements while maintaining strong consistency.
This paper presents OptBench, an interactive workbench built on DuckDB that enables researchers and practitioners to transparently build, benchmark, and visually compare query optimizers for hybrid SQL and AI/ML workloads through a unified platform.
This paper introduces \texttt{EinSum}, a tensor-relational extension of Einstein Summation Notation that automatically rewrites computations to leverage efficient numerical kernels for dense operations while utilizing relational systems to manage large-scale sparsity.
This paper introduces DataFactory, a collaborative multi-agent framework that overcomes the context, hallucination, and reasoning limitations of existing TableQA systems by orchestrating specialized agents for structured and relational reasoning, thereby achieving significant accuracy improvements across multiple benchmarks.
The paper introduces OLLA, an LLM-driven online aggregation framework that accelerates unstructured text analytics by incrementally processing data and employing semantic stratified sampling to achieve high accuracy with significantly reduced latency compared to full-batch processing.
The paper introduces PRIME, a two-stage iterative algorithm that combines pruned graph search with an Adaptive Sign Gradient Method to efficiently solve the Token Graph Routing Problem on blockchain platforms, significantly outperforming existing baselines like Uniswap in both execution price and computational speed.
The paper proposes CEMR, a novel subgraph matching algorithm that significantly improves efficiency on large graphs by eliminating redundant computations through common extension merging and reusing, while further optimizing performance with two pruning techniques.
This paper introduces Samyama, a high-performance, unified graph-vector database written in Rust that integrates persistent storage, vector indexing, native optimization solvers, and agentic LLM enrichment into a single engine, achieving competitive throughput and latency on commodity hardware while offering GPU-accelerated enterprise features.
This paper proposes DMRAL, a decomposition-driven framework that constructs a table relationship graph and employs aligned question decomposition with coverage-aware retrieval and sub-question guided reasoning to significantly outperform existing methods in numerical multi-table question answering over large-scale table collections.
This paper introduces Structured Gossip DNS, a partition-resilient name resolution system for large-scale dynamic networks that leverages DHT finger tables and passive stabilization to achieve eventual consistency with reduced message complexity and without requiring global coordination.
The paper proposes GP-Tree, a novel in-memory spatial index that combines adaptive grid cells with a prefix tree structure to replace coarse minimum bounding rectangles with fine-grained approximations, thereby significantly improving filtering accuracy and query performance for complex spatial objects compared to traditional indexes.