Suchergebnisse - "Computer Science - Databases"

Any-k Algorithms for Enumerating Ranked Answers to Conjunctive Queries

Autoren: Nikolaos Tziavelis Wolfgang Gatterbauer Mirek Riedewald

Quelle: ACM Transactions on Database Systems. 51:1-47

Schlagwörter: FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Computer Science - Databases, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, Databases (cs.DB), Data Structures and Algorithms (cs.DS), 02 engineering and technology, Logic in Computer Science (cs.LO)

Zugangs-URL: http://arxiv.org/abs/2205.05649

Steering Semantic Data Processing With DocWrangler

Autoren: Shreya Shankar Bhavya Chopra Mawil Hasan et al.

Quelle: Proceedings of the 38th Annual ACM Symposium on User Interface Software and Technology. :1-18

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Computer Science - Human-Computer Interaction, Databases (cs.DB), Human-Computer Interaction (cs.HC)

Zugangs-URL: http://arxiv.org/abs/2504.14764

In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration

Autoren: Jiajie Fu Haitong Tang Arijit Khan et al.

Quelle: Proceedings of the ACM on Management of Data. 3:1-28

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB)

Zugangs-URL: http://arxiv.org/abs/2506.02509

Differentially Private Explanations for Clusters

Autoren: Amir Gilad Tova Milo Kathy Razmadze et al.

Quelle: Proceedings of the ACM on Management of Data. 3:1-27

Schlagwörter: FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer Science - Databases, Databases (cs.DB), Cryptography and Security (cs.CR)

Zugangs-URL: http://arxiv.org/abs/2506.05900

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index

Autoren: Yirui Zhan Wen Nie Jun Gao

Quelle: Proceedings of the ACM on Management of Data. 3:1-24

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB)

Zugangs-URL: http://arxiv.org/abs/2505.24312

VecFlow: A High-Performance Vector Data Management System for Filtered-Search on GPUs

Autoren: Jingyi Xi Chenghao Mo Ben Karsin et al.

Quelle: Proceedings of the ACM on Management of Data. 3:1-27

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB)

Zugangs-URL: http://arxiv.org/abs/2506.00812

Compressing Integer Lists with Contextual Arithmetic Trits

Autoren: Yann Barsamian André Chailloux

Quelle: ACM Transactions on Information Systems. 43:1-29

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Computer Science - Information Theory, Information Theory (cs.IT), 0202 electrical engineering, electronic engineering, information engineering, Databases (cs.DB), 02 engineering and technology

Zugangs-URL: http://arxiv.org/abs/2209.02089

Enhancing healthcare recommendation systems with multimodal LLMs-based MOE architecture

Autoren: Xu, Jingyu Wang, Yang

Quelle: IET Conference Proceedings. 2025:123-129

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB), Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2412.11557

Towards Sequence Utility Maximization under Utility Occupancy Measure

Autoren: Gengsen Huang Wensheng Gan Philip S. Yu

Quelle: ACM Transactions on Knowledge Discovery from Data. 19:1-27

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Databases, Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Databases (cs.DB), 02 engineering and technology

Zugangs-URL: http://arxiv.org/abs/2212.10452

Finding Counterfactual Evidences for Node Classification

Autoren: Dazhuo Qiu Jinwen Chen Arijit Khan et al.

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :2362-2373

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Databases, Databases (cs.DB), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2505.11396

Rank It, Then Ask It: Input Reranking for Maximizing the Performance of LLMs on Symmetric Tasks

Autoren: Mohsen Dehghankar Abolfazl Asudeh

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :451-462

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Databases, Databases (cs.DB), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2412.00546

Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey

Autoren: Yuxuan Liang Haomin Wen Yutong Xia et al.

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :6063-6073

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Databases, Databases (cs.DB), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2503.13502

ChatPD: An LLM-driven Paper-Dataset Networking System

Autoren: Anjie Xu Ruiqing Ding Leye Wang

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :5106-5116

Schlagwörter: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Databases, Computer Science - Artificial Intelligence, Databases (cs.DB), Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2505.22349

FLEXIS: FLEXible Frequent Subgraph Mining using Maximal Independent Sets

Autoren: Akshit Sharma Sam Reinehr Dinesh Mehta et al.

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :2514-2525

Schlagwörter: Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Performance, Computer Science - Databases, Databases (cs.DB)

Zugangs-URL: http://arxiv.org/abs/2404.01585

Federated Continual Graph Learning

Autoren: Yinlin Zhu Miao Hu Di Wu

Quelle: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2. :4203-4213

Schlagwörter: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks, Databases (cs.DB), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2411.18919

Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

Autoren: Dorbani, Anas Yasser, Sunny Lin, Jimmy et al.

Quelle: Proceedings of the VLDB Endowment. 18:5415-5418

Schlagwörter: FOS: Computer and information sciences, Natural Language Processing Techniques, Speech Recognition and Synthesis, Computer Science - Databases, Artificial Intelligence, Topic Modeling, Databases (cs.DB), Information Retrieval (cs.IR), Computer Science - Information Retrieval

Zugangs-URL: http://arxiv.org/abs/2504.01157

VIDEX: A Disaggregated and Extensible Virtual Index for the Cloud and AI Era

Autoren: Kang, Rong Wang, Shuai Zhang, Tieying et al.

Quelle: Proceedings of the VLDB Endowment. 18:5231-5234

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Databases (cs.DB)

Zugangs-URL: http://arxiv.org/abs/2503.23776

Top-K Representative Search for Comparative Tree Summarization

Autoren: Yuqi Chen Xin Huang Bilian Chen

Quelle: IEEE Transactions on Knowledge and Data Engineering. 37:4873-4879

Schlagwörter: Computer Science - Databases

Zugangs-URL: http://arxiv.org/abs/2407.14098

Rel: A Programming Language for Relational Data

Autoren: Aref, Molham Guagliardo, Paolo Kastrinis, George et al.

Weitere Verfasser: Aref, Molham Guagliardo, Paolo Kastrinis, George et al.

Quelle: Companion of the 2025 International Conference on Management of Data. :283-296

Schlagwörter: FOS: Computer and information sciences, Relational data model, Computer Science - Programming Languages, Computer Science - Databases, Impedance mismatch, Programming in the large, Query language design, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Relational knowledge graph, Databases (cs.DB), Relational pro- gramming, Graph normal form, Programming Languages (cs.PL)

Zugangs-URL: http://arxiv.org/abs/2504.10323

TableDC: Deep Clustering for Tabular Data: Deep Clustering for Tabular Data

Autoren: Hafiz Tayyab Rauf André Freitas Norman William Paton

Quelle: Rauf, H T, Freitas, A & Paton, N 2025, TableDC : Deep Clustering for Tabular Data. in Proceedings of the ACM on Management of Data. 3 edn, vol. 3, 229, Association for Computing Machinery, pp. 1-28.

Schlagwörter: FOS: Computer and information sciences, Computer Science - Databases, Data integration, Databases (cs.DB), deep clustering, Mahalanobis distance, data cleaning

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2405.17723
https://research.manchester.ac.uk/en/publications/4daa0e19-8c1e-4351-b23b-de7e31ab5d13
https://doi.org/10.1145/3725366
https://research.manchester.ac.uk/en/publications/4daa0e19-8c1e-4351-b23b-de7e31ab5d13
https://pure.manchester.ac.uk/ws/files/361658222/2405.17723v1.pdf