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AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Jiahao He, Yutao Cui, Cuiping Li, Jikang Jiang, Yuheng Hou, Hong Chen
https://arxiv.org/abs/2510.10580 https://

AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Recent studies have identified two main approaches to improve query optimization: learned query optimization (LQO), which generates or selects better query plans before execution based on models trained in advance, and adaptive query processing (AQP), which adapts the query plan during execution based on statistical feedback collected at runtime. Although both approaches have shown promise, they also face critical limitations. LQO must commit to a fixed plan without access to actual cardinaliti…

Geolog-IA: Conversational System for Academic Theses
Micaela Fuel Pozo, Andrea Guatumillo Saltos, Yese\~na Tipan Llumiquinga, Kelly Lascano Aguirre, Marilyn Castillo Jara, Christian Mejia-Escobar
https://arxiv.org/abs/2510.02653

Geolog-IA: Conversational System for Academic Theses
This study presents the development of Geolog-IA, a novel conversational system based on artificial intelligence that responds naturally to questions about geology theses from the Central University of Ecuador. Our proposal uses the Llama 3.1 and Gemini 2.5 language models, which are complemented by a Retrieval Augmented Generation (RAG) architecture and an SQLite database. This strategy allows us to overcome problems such as hallucinations and outdated knowledge. The evaluation of Geolog-IA's …