skip to main content
10.1145/3663742acmconferencesBook PagePublication PagesmodConference Proceedingsconference-collections
aiDM '24: Proceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management
ACM2024 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGMOD/PODS '24: International Conference on Management of Data Santiago AA Chile 14 June 2024
ISBN:
979-8-4007-0680-6
Published:
09 June 2024
Sponsors:
Recommend ACM DL
ALREADY A SUBSCRIBER?SIGN IN

Reflects downloads up to 17 Jan 2025Bibliometrics
Abstract

No abstract available.

Skip Table Of Content Section
research-article
Open Access
Smart Science Needs Linked Open Data with a Dash of Large Language Models and Extended Relations
Article No.: 1, Pages 1–11https://doi.org/10.1145/3663742.3663971

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 ...

short-paper
Rethinking Table Retrieval from Data Lakes
Article No.: 2, Pages 1–5https://doi.org/10.1145/3663742.3663972

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 ...

short-paper
Open Access
Mallet: SQL Dialect Translation with LLM Rule Generation
Article No.: 3, Pages 1–5https://doi.org/10.1145/3663742.3663973

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 ...

short-paper
Low Rank Approximation for Learned Query Optimization
Article No.: 4, Pages 1–5https://doi.org/10.1145/3663742.3663974

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 ...

short-paper
Open Access
Learning Bit Allocations for Z-Order Layouts in Analytic Data Systems
Article No.: 5, Pages 1–9https://doi.org/10.1145/3663742.3663975

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 ...

Recommendations

Acceptance Rates

Overall Acceptance Rate 19 of 26 submissions, 73%
YearSubmittedAcceptedRate
aiDM '2066100%
aiDM '1912867%
aiDM'188563%
Overall261973%