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VLDB Workshops 2024: Guangzhou, China
- Proceedings of Workshops at the 50th International Conference on Very Large Data Bases, VLDB 2024, Guangzhou, China, August 26-30, 2024. VLDB.org 2024
ADMS 2024: 15th Workshop on Accelerating Analytics and Data Management Systems using Modern Processor and Storage Architectures
- Wentao Huang, Mo Sha, Mian Lu, Yuqiang Chen, Bingsheng He, Kian-Lee Tan:
Bandwidth Expansion via CXL: A Pathway to Accelerating In-Memory Analytical Processing. - Julia Spindler, Philipp Fent, Adrian Riedl, Thomas Neumann:
Can Delta Compete with Frame-of-Reference for Lightweight Integer Compression? - Alessandro Fogli, Peter Pletzuch, Jana Giceva:
Optimizing Sorting for Chiplet-Based CPUs. - Hendrik Makait, Bonaventura Del Monte, Tilmann Rabl:
Ghostwriter: a Distributed Message Broker on RDMA and NVM.
BigVis 2024: 7th International Workshop on Big Data Visual Exploration and Analytics
- Natalia V. Andrienko, Gennady L. Andrienko, Dimitris Zissis, Alexandros Troupiotis-Kapeliaris, Giannis Spiliopoulos:
Techniques for interactive visual examination of autonomous vessel performance. - Théo Bouganim, Ioana Manolescu, Emmanuel Pietriga:
A Unified Visual Exploration Framework for (Semi-)structured Data. - Xin Zhang, Ahmed Eldawy:
QPV: An Input Control Component For Progressive Visualization Analytics. - Yu Wang, Jing Lu, Le Liu, Junping Zhang, Siming Chen:
InterpretStack: Interpretable Exploration and Interactive Visualization Construction of Stacking Algorithm. - Rubab Zahra Sarfraz, Samar Haider:
Vizard: Improving Visual Data Literacy with Large Language Models. - Mei Wang, Hai-Ning Liang, Yu Liu, Chengtao Ji, Lingyun Yu:
Tangible Progress: Employing Visual Metaphors and Physical Interfaces in AI-based English Language Learning. - Xu Yang, Yiheng Liang, Le Liu, Lianwei Wu, Xiaoru Yuan:
EvalGPT: A Visual Analytic Framework for Enhancing Trust in Large Language Models. - Stavros Maroulis, Nikos Bikakis, Vasileios Stamatopoulos, George Papastefanatos:
Partial Adaptive Indexing for Approximate Query Answering. - Chang Yuan Lang Teng, Zhiwei Shi, Lingyun Yu, Yu Liu:
Enhancing Geographic Information Visualization: A Comparative Analysis of Digital Maps and Projection Augmented Relief Maps.
DATAI 2024: 1st International Workshop on Data-driven AI
- Ilin Tolovski, Tilmann Rabl:
Addressing Data Management Challenges for Interoperable Data Science. - Xiang Huang, Shuang Hao:
Missing Value Imputation via Pre-trained Language Models with Trainable Prompt and Retrieval Augmentation. - Chenjie Li, Dan Zhang, Jin Wang:
LLM-assisted Labeling Function Generation for Semantic Type Detection. - Jinqi Liu, Anzhen Zhang, Jiajia Li, Na Guo, Jing Zhang:
Approximate Functional Dependencies Discovery Using Markov Blanket.
FAB 2024: 6th International Workshop on Foundations and Applications of Blockchain
- Mohammad Javad Amiri:
Proceedings of the Sixth International Workshop on Foundations and Applications of Blockchain (FAB). - Ruben Mayer:
Benefits and Challenges of Decentralization in Data Systems: Opportunities for Data Management Research. - Arijit Khan:
Data Management and AI for Blockchain Data Analysis: A Round Trip and Opportunities. - Lan Lu, Tao Luo, Jingyi Li, Hongxun Ding, Brendan Massey, Haoxian Chen, Boon Thau Loo:
Practical Declarative Smart Contracts Optimization. - Avishek De, Divyakant Agrawal, Amr El Abbadi:
CroCRPC: Cross-Chain Remote Procedure Calls Framework for dApps. - Giorgos Demosthenous, Chryssis Georgiou, Eliada Polydorou:
From On-chain to Macro: Assessing the Importance of Data Source Diversity in Cryptocurrency Market Forecasting. - Asma Jodeiri Akbarfam, Hoda Maleki:
SOK: Blockchain for Provenance. - Jeeta Ann Chacko:
A Comprehensive Outlook for Analyzing and Enhancing the Performance of Blockchain Platforms.
LLM+KG 2024: 1st International Workshop on Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs
- Arijit Khan, Tianxing Wu, Xi Chen:
LLM+KG: Data Management Opportunities in Unifying Large Language Models + Knowledge Graphs. - Ningyu Zhang, Zekun Xi, Yujie Luo, Peng Wang, Bozhong Tian, Yunzhi Yao, Jintian Zhang, Shumin Deng, Mengshu Sun, Lei Liang, Zhiqiang Zhang, Xiaowei Zhu, Jun Zhou, Huajun Chen:
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System. - Jixuan Nie, Xia Hou, Wenfeng Song, Xuan Wang, Xingliang Jin, Xinyu Zhang, ShuoZhe Zhang, Jiaqi Shi:
Knowledge Graph Efficient Construction: Embedding Chain-of-Thought into LLMs. - Yusuf Abdulle, Emily Groves, Minhong Wang, Holger Kunz, Jason Hoelscher-Obermaier, Ronin Wu, Honghan Wu:
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest. - Yongli Mou, Li Liu, Sulayman K. Sowe, Diego Collarana, Stefan Decker:
Leveraging LLMs Few-shot Learning to Improve Instruction-driven Knowledge Graph Construction. - Hanieh Khorashadizadeh, Fatima Zahra Amara, Morteza Kamaladdini Ezzabady, Frédéric Ieng, Sanju Tiwari, Nandana Mihindukulasooriya, Jinghua Groppe, Soror Sahri, Farah Benamara, Sven Groppe:
Research Trends for the Interplay between Large Language Models and Knowledge Graphs. - Xinfu Liu, Yirui Wu, Yuting Zhou, Junyang Chen, Huan Wang, Ye Liu, Shaohua Wan:
Enhancing Large Language Models with Multimodality and Knowledge Graphs for Hallucination-free Open-set Object Recognition. - Emanuele Cavalleri, Mauricio Soto Gomez, Ali Pashaeibarough, Dario Malchiodi, J. Harry Caufield, Justin T. Reese, Chris Mungall, Peter N. Robinson, Elena Casiraghi, Giorgio Valentini, Marco Mesiti:
SPIREX: Improving LLM-based relation extraction from RNA-focused scientific literature using graph machine learning. - Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen:
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration. - Daham M. Mustafa, Abhishek Nadgeri, Diego Collarana, Benedikt T. Arnold, Christoph Quix, Christoph Lange, Stefan Decker:
From Instructions to ODRL Usage Policies: An Ontology Guided Approach.
QDB 2024: 13th International Workshop on Quality in Databases
- Sourav S. Bhowmick, Lisa Ehrlinger, Hazar Harmouch:
13th International Workshop on Quality in Databases: Preface. - Fatemeh Ahmadi, Yusuf Mandirali, Ziawasch Abedjan:
Accelerating the Data Cleaning Systems Raha and Baran through Task and Data Parallelism. - Rubab Zahra Sarfraz:
Towards Semi-Supervised Data Quality Detection In Graphs. - Ekta Pradhan, Romila Pradhan:
Valuation-based Data Acquisition for Machine Learning Fairness. - Tingyan Ma, Wei Liu, Bin Lu, Xiaoying Gan, Yunqiang Zhu, Luoyi Fu, Chenghu Zhou:
AutoFAIR : Automatic Data FAIRification via Machine Reading. - Yu Liu, Jiangnan Cheng, Steve Chuck, Lyublena Antova, Yurgis Baykshtis, Matt David, Ge Gao, Mehrdad Honarkhah, Kuan-Sung Huang, Chen-Kuei Lee, Usman Muhammad, Shihao Peng, Andrii Rosa, Rebecca Schlussel, Michael Shang, Kelvin Silva, Brandon Vo, Zac Wen, Yihao Zhou:
Compute Engine Testing with Privacy-Compliant Production-Like Synthetic Data. - Liam Tirpitz, Leon Gentges:
Process Model-based Access Control Policies for Cross-Organizational Data Sharing. - Samuele Langhi, Angela Bonifati, Riccardo Tommasini:
Tracking Consistency over Data Streams with InkStream [Demo]. - Jongjun Park, Akanksha Nehete, Tammy Zeng, Fei Chiang:
A Data Generator to Explore the Interactions Between Concept Drifts and Anomalies [Demo].
QDSM 2024: 2nd International Workshop on Quantum Data Science and Management
- Valter Uotila, Sven Groppe, Le Gruenwald, Jiaheng Lu, Wolfgang Mauerer:
Workshop Summary of the Second International Workshop on Quantum Data Science and Management (QDSM). - Tuodu Li, Gongsheng Yuan, Chang Yao, Meng Shi, Ziyue Wang, Ling Qian, Jiaheng Lu:
Quantum Storage Design for Tables in RDBMS. - Martin Vogrin, Rok Vogrin, Sven Groppe, Jinghua Groppe:
Supervised Learning on Relational Databases with Quantum Graph Neural Networks. - Tim Littau, Ziyu Li, Rihan Hai:
Quantum Data Structures for Enhanced Database Performance. - Lauri Vuorenkoski, Valter Uotila:
Graphs on Qubits: Demonstrating Three Graph Algorithms on Quantum Computers. - Manish Kesarwani, Jayant R. Haritsa:
Is Quantum-Based SQL Query Execution Viable?
TaDa 2024: 2nd International Workshop on Tabular Data Analysis
- Vasilis Efthymiou, Sainyam Galhotra, Oktie Hassanzadeh, Chuan Lei, Kavitha Srinivas:
2nd International Workshop on Tabular Data Analysis (TaDA). - Hasan Jamil:
Toward a Declarative Query Language for Machine Learning. - Koyena Pal, Aamod Khatiwada, Roee Shraga, Renée J. Miller:
ALT-GEN: Benchmarking Table Union Search using Large Language Models. - Jan-Micha Bodensohn, Ulf Brackmann, Liane Vogel, Matthias Urban, Anupam Sanghi, Carsten Binnig:
LLMs for Data Engineering on Enterprise Data. - Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi, Christos Diou:
Fast and Accurate Regional Effect Plots for Automated Tabular Data Analysis. - Zezhou Huang, Jia Guo, Eugene Wu:
Transform Table to Database Using Large Language Models. - Han Zhang, Quan Gan, David Wipf, Weinan Zhang:
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases. - Marcel Parciak, Brecht Vandevoort, Frank Neven, Liesbet M. Peeters, Stijn Vansummeren:
Schema Matching with Large Language Models: an Experimental Study. - Grace Fan, Roee Shraga, Renée J. Miller:
Finding Support for Tabular LLM Outputs. - Minjie Wang, Quan Gan, David Wipf, Zhenkun Cai, Ning Li, Jianheng Tang, Yanlin Zhang, Zizhao Zhang, Zunyao Mao, Yakun Song, Yanbo Wang, Jiahang Li, Han Zhang, Guang Yang, Xiao Qin, Chuan Lei, Muhan Zhang, Weinan Zhang, Christos Faloutsos, Zheng Zhang:
4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs. - Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada:
Large Language Models as Data Preprocessors. - Kayvon Heravi, Saathvik Dirisala, Babak Salimi:
DEMA: Enhancing Causal Analysis through Data Enrichment and Discovery in Data Lakes. - Carolina Cortes, Camila Sanz, Lorena Etcheverry, Adriana Marotta:
Data Quality Management for Responsible AI in Data Lakes. - Alex Bäuerle, Çagatay Demiralp, Michael Stonebraker:
Humboldt: Metadata-Driven Extensible Data Discovery.
LSGDA 2024: 3rd International Workshop on Large-Scale Graph Data Analytics
- Long Yuan, Zhengyi Yang, Qingqiang Sun, Alexander Zhou:
Report on the 3rd International Workshop on Large-Scale Graph Data Analytics (LSGDA 2024). - Jan Appel, Andreas Weiler:
XCrowd: Real-Time Dynamic Crowd Movement Simulation on Graph Networks. - Hanchen Qiu, Haojia Zhu, Zhicheng Li, Jiahui Jin:
MRG-SER: Self-supervised Spatial Entity Resolution Based on Multi-Relational Graph. - Bingqing Lyu, Xiaoli Zhou, Longbin Lai, Yufan Yang, Yunkai Lou, Yongfei Liu:
Enhancing Neo4j Query Efficiency with Seamless Integration of the GOpt Optimization Framework. - Barbara Hoffmann, Jana Vatter, Ruben Mayer:
Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act. - Jana Vatter, Maurice L. Rochau, Ruben Mayer, Hans-Arno Jacobsen:
Size Does (Not) Matter? Sparsification and Graph Neural Network Sampling for Large-scale Graphs. - Xingyun Chen, Yan Huang, Zhenzhen Xie, Junjie Pang:
HyperFedNet: Communication-Efficient Personalized Federated Learning Via Hypernetwork. - Chen Chen, Jingya Qian, Hui Luo, Yongye Li, Xiaoyang Wang:
Parallel Higher-order Truss Decomposition. - Yang Liu, Xin Wang, Jiake Ge, Hui Wang, Dawei Xu, Yongzhe Jia:
Text to Graph Query Using Filter Condition Attributes.
CloudDb 2024: 2nd Workshop on Cloud Databases
- Hanwen Liu, Mihail Stoian, Alexander van Renen, Andreas Kipf:
Corra: Correlation-Aware Column Compression. - Alireza Heidari, Amirhossein Ahmadi, Zefeng Zhi, Wei Zhang:
MetaHive: A Cache-Optimized Metadata Management for Heterogeneous Key-Value Stores.
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