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Smart Science Needs Linked Open Data with a Dash of Large Language Models and Extended Relations
Quality scientific inquiries depend on access to data distributed over the entire globe. Linked open data (LOD) and FAIRness play major roles in ensuring access to data that scientists need to answer interesting questions. However, a data model and a ...
Rethinking Table Retrieval from Data Lakes
Table retrieval from data lakes has recently become important for many downstream tasks, including data discovery and table question answering. Existing table retrieval approaches estimate each table's relevance to a particular information need and ...
Mallet: SQL Dialect Translation with LLM Rule Generation
Translating between the SQL dialects of different systems is important for migration and federated query processing. Existing approaches rely on hand-crafted translation rules, which tend to be incomplete and hard to maintain, especially as the number of ...
Low Rank Approximation for Learned Query Optimization
We present LimeQO, a learned steering query optimizer based on linear methods, such as matrix completion, for repetitive workloads. LimeQO can forgo expensive neural networks by taking advantage of the low-rank structure of query workloads. Using offline ...
Learning Bit Allocations for Z-Order Layouts in Analytic Data Systems
To improve the performance of scanning and filtering, modern analytic data systems such as Amazon Redshift and Databricks Delta Lake give users the ability to sort a table using a Z-order, which maps each row to a "Z-value" by interleaving the binary ...