83 papers
This paper presents a holistic resiliency framework for Apache Pinot at LinkedIn, featuring Query Workload Isolation, Impact-Free Rebalancing, Maintenance Zone Awareness, and Adaptive Server Selection, which collectively ensure stable subsecond query latency and high availability for petabyte-scale OLAP workloads under failures and load spikes.
This paper introduces Tursio, a secure, on-premises platform that empowers credit unions to query complex structured databases using natural language by leveraging Large Language Models to automatically generate semantic knowledge graphs and compliant query plans.
LLM-FK is a novel multi-agent framework that overcomes the limitations of conventional heuristic and naive LLM methods in detecting foreign keys within large-scale complex databases by coordinating specialized agents to prune the search space, enhance reasoning with domain knowledge, and ensure global schema consistency, thereby achieving superior accuracy and scalability.
This paper introduces sketch-oriented databases, a categorical framework that unifies various graph-based paradigms and features through finite-limit sketches, while proposing localizers for lazy path inference and stuttering sketches to enable modular composition and scalable model growth.
This paper addresses the unexplored challenge of avoiding redundancy in data lakes by defining the Novel Table Search (NTS) problem, proposing an efficient penalization-based approximation algorithm called ANTs, and demonstrating through extensive experiments that it outperforms existing methods in capturing syntactic novelty while maintaining low execution time.
This paper introduces the Fifth Graph Normal Form (5GNF), a trait-based framework that normalizes property graph metadata by extracting recurring attributes into shared Trait Nodes, thereby reducing redundancy and improving semantic clarity while maintaining competitive query performance.
This paper introduces DCTR, a table retrieval mechanism that leverages fine-grained typed query decomposition and global connectivity awareness to effectively handle complex, open-domain question answering over relational databases, demonstrating robustness on industry-aligned benchmarks.
This paper introduces TableEG, a framework that leverages fine-tuned large language models to generate authentic, distribution-aligned synthetic errors in tabular data, thereby addressing the scarcity of real-world error datasets and establishing a robust benchmark for evaluating data cleaning techniques.
This paper introduces MMTU, a large-scale benchmark comprising over 28,000 questions across 25 real-world expert-level table tasks, designed to comprehensively evaluate and reveal the significant limitations of current frontier models in understanding, reasoning, and manipulating structured tabular data.
This paper introduces WikiDBGraph, a large-scale benchmark suite derived from 100,000 real-world relational databases, designed to evaluate and expose the limitations of existing collaborative learning frameworks in handling the complex, unaligned, and interconnected nature of practical data silos.
SDFed is a heterogeneous federated prompt learning framework that addresses local-global discrepancies by combining a fixed-length global prompt with variable-length local prompts, enhanced by subspace refinement and divergence control strategies to improve performance and robustness in privacy-sensitive, resource-constrained multi-party settings.
This paper introduces Rel-MOSS, a novel relation-centric deep learning framework that addresses the critical issue of class imbalance in relational databases by employing a relation-wise gating controller and a relation-guided minority synthesizer to enhance the representation and over-sampling of minority entities, thereby significantly outperforming existing methods in entity classification tasks.
This paper introduces Dial, a knowledge-grounded framework that addresses the challenges of generating executable SQL across heterogeneous database systems by employing dialect-aware logical planning, a hierarchical intent-aware knowledge base, and an execution-driven debugging loop, achieving significant improvements in translation accuracy and dialect feature coverage on the newly constructed DS-NL2SQL benchmark.
This paper introduces Projection-Augmented Graph (PAG), a novel Approximate Nearest Neighbor Search framework that integrates projection techniques into graph indexing to simultaneously achieve high query efficiency, fast indexing, low memory usage, and robust scalability across modern AI workloads, outperforming existing methods like HNSW by up to 5x in speed while supporting online insertions.
This paper demonstrates that graph sparsification serves as an effective, lightweight preprocessing step for Graph Neural Networks, significantly accelerating training and inference on large-scale graphs while often preserving or even improving predictive accuracy.
This paper introduces a nearly instance-optimal algorithm for Poisson sampling over acyclic joins that utilizes a random-access index to efficiently generate samples without fully materializing the join result, demonstrating superior performance over existing methods while also serving as a competitive foundation for classical join processing.
This paper introduces a compressed tree-based data hypercube representation within the Polytope framework to efficiently model irregular and branching Earth science datasets, enabling a unified system for scalable, user-centric feature extraction that overcomes the limitations of traditional orthogonal datacube models.
Pneuma-Seeker is an LLM-powered agentic system that aligns AI with human data needs by iteratively refining a shared relational schema to concretely represent evolving information requirements, thereby reducing hallucinations and improving answer accuracy over heterogeneous relational data.
This paper introduces ExBI, a novel hypergraph-based framework for Exploratory Business Intelligence that overcomes traditional limitations of static schemas and high computational costs through dynamic schema evolution and sampling-based algorithms, achieving significant speedups over existing systems like Neo4j and MySQL while maintaining high analytical accuracy.
This paper addresses the challenges of evaluating Computer Graphics image quality by constructing a new dataset with systematic quality descriptions and proposing a retrieval-augmented, two-stream framework that significantly enhances Vision Language Models' ability to assess and explain CG quality.