Temporal Query
Temporal query processing focuses on retrieving information from data sources where time is a critical factor, aiming to efficiently and accurately answer questions involving temporal relationships and constraints. Current research emphasizes developing robust algorithms and models, such as those incorporating multi-scale patching for time series forecasting or employing logic-based frameworks for reasoning over temporal knowledge graphs, to handle the complexities of temporal data. These advancements are crucial for improving the accuracy and explainability of systems dealing with time-dependent information, with applications ranging from knowledge graph completion and question answering to multi-agent systems and decision-making under temporal constraints.