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Evaluation Trade-Offs for Acyclic Conjunctive Queries.

, , , and . CSL, volume 252 of LIPIcs, page 29:1-29:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2023)

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Tractable Conjunctive Queries over Static and Dynamic Relations., , , , and . CoRR, (2024)Distributed Discrete Event Simulation Architecture with Connectors., , and . SIMULTECH, page 130-137. SciTePress, (2015)A deep learning framework with convolutional long short-term memory for influenza-like illness trend estimation.. Concurr. Comput. Pract. Exp., (2022)A Methodology for cross-resolution modeling in DEVS using event-B refinement ; DEVS için melez-çözünürlüklü modellemede event-b arıtımı kullanan bir yöntembilimi.. Middle East Technical University, Çankaya, Ankara, Turkey, (2014)base-search.net (ftmetuankair:oai:https://open.metu.edu.tr:11511/23257).Genetic algorithm optimized a deep learning method with attention mechanism for soil moisture prediction., , , and . Neural Comput. Appl., 36 (4): 1761-1772 (February 2024)F-IVM: Learning over Fast-Evolving Relational Data., , , and . SIGMOD Conference, page 2773-2776. ACM, (2020)Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries., , , and . PODS, page 375-392. ACM, (2020)Conjunctive Queries with Free Access Patterns Under Updates., , , and . ICDT, volume 255 of LIPIcs, page 17:1-17:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2023)Evaluation Trade-Offs for Acyclic Conjunctive Queries., , , and . CSL, volume 252 of LIPIcs, page 29:1-29:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2023)Counting Triangles under Updates in Worst-Case Optimal Time., , , , and . ICDT, volume 127 of LIPIcs, page 4:1-4:18. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)