84 papers
CRISP is a novel framework for very-high-dimensional approximate nearest neighbor search that combines a lightweight, correlation-aware adaptive variance redistribution strategy with a cache-coherent CSR index and a dual-mode query engine to achieve state-of-the-art throughput, low construction costs, and peak memory efficiency.
RESYSTANCE is a system that leverages eBPF and io_uring to offload LSM-tree compaction I/O operations directly into the kernel, eliminating nearly all system call overhead and achieving up to 75% throughput improvement and 40% latency reduction in write-intensive workloads without modifying existing database structures.
This paper presents FluxSieve, a unified architecture that embeds lightweight in-stream precomputation and filtering into the data ingestion path to reconcile streaming and analytical data planes, thereby achieving orders-of-magnitude improvements in query performance for large-scale cloud observability with negligible overhead.
This paper introduces DEBISS, a novel corpus of spoken and individual semi-structured debates designed to address the scarcity of debate datasets by providing comprehensive NLP annotations for tasks such as speech-to-text, speaker diarization, argument mining, and debater quality assessment.
This paper presents a deterministic, rule-based Python workflow that processes administrative academic data to generate reproducible cost-per-student reports enriched with an interpretable fuzzy banding system for classifying school-year performance into Low, Medium, or High categories.
This paper proposes Clustering-Sampling-Voting (CSV), a novel framework that significantly reduces the linear latency and token costs of semantic filtering in large language models by embedding tuples into semantic clusters, sampling subsets for evaluation, and inferring cluster-level labels through voting strategies, thereby achieving sublinear complexity with strong error guarantees.
This paper introduces the FFBtree, a B+-tree variant that eliminates performance fluctuations caused by split propagation by preemptively splitting critical nodes, thereby guaranteeing consistent I/O costs for insert operations while maintaining concurrency and efficiency.
This paper introduces CONE, a hybrid transformer encoder that utilizes a novel composite embedding algorithm to preserve the semantics of units and variables for complex numerical data, achieving state-of-the-art performance in numerical reasoning tasks across diverse domains.
This paper addresses the computational challenges of generalizing fair top- selection to multiple protected groups while minimizing disparity from a reference function, revealing new hardness barriers for small and proposing an efficient, robust two-pronged solution that incorporates utility loss as an alternative disparity measure.
This paper introduces KG-WISE, an LLM-guided inference system that accelerates Graph Neural Network queries on large Knowledge Graphs by dynamically decomposing models into fine-grained components and retrieving only semantically relevant subgraphs, achieving up to 28x faster inference and 98% lower memory usage while maintaining accuracy.
The paper proposes Stratum, a unified system infrastructure that decouples pipeline execution from agent reasoning to efficiently scale agentic ML pipeline search by integrating with existing Python libraries and utilizing an optimized Rust-based runtime, achieving up to 16.6x speedup.
V3DB is a verifiable vector-search service that enables clients to audit the correctness of approximate nearest-neighbor retrieval results from untrusted providers by generating succinct zero-knowledge proofs for committed snapshots without revealing the underlying data, achieving significant performance improvements through an optimized proving strategy that avoids costly in-circuit sorting and random access.
This scientometric analysis of Avian Influenza research from 2014 to 2023 reveals a steady global increase in publications led by Chinese and American institutions, highlighting key journals, collaborative networks, and the predominance of original articles to underscore the necessity for continued international cooperation.
This paper introduces xBound, the first theoretical framework for computing provable join size lower bounds, which addresses the critical issue of cardinality underestimation in production databases by correcting 23.6% of errors and delivering up to 20.1x query speedups on Microsoft's Fabric Data Warehouse.
This paper introduces HIT-Leiden, a novel algorithm that efficiently maintains high-quality Leiden communities in large dynamic graphs by bounding the scope of updates through hierarchical structures, achieving speedups of up to five orders of magnitude over existing methods while meeting stringent production latency requirements.
The paper proposes LQRS, a learned query re-optimization framework for Spark SQL that leverages a curriculum reinforcement learning strategy and runtime observations to dynamically refine execution plans, achieving up to a 90% reduction in end-to-end execution time compared to existing methods.
This paper introduces TigerVector, a system integrated into TigerGraph v4.2 that unifies vector search and graph querying through an MPP index framework and enhanced GSQL language, demonstrating superior hybrid search performance and scalability compared to leading graph and vector databases.
This paper introduces PANDAExpress, a novel algorithm that eliminates the impractical polylogarithmic factor of the original PANDA framework by employing a new probabilistic inequality and a dynamic hyperplane partitioning scheme, thereby achieving optimal, specialized-algorithm-level runtimes for conjunctive queries and disjunctive datalog rules under arbitrary degree constraints while maintaining full generality.
This paper proposes ErrorLLM, a framework that explicitly models text-to-SQL errors using structural features and dedicated error tokens to overcome the limitations of existing self-debugging and self-correction paradigms, thereby significantly improving SQL refinement through high-precision error detection and guided structural correction.
SpotIt+ is an open-source tool that evaluates Text-to-SQL systems by actively generating database counterexamples to verify query equivalence, utilizing a novel constraint-mining pipeline to ensure these differentiating instances reflect practically relevant discrepancies missed by standard testing.