Advances in Data Mining and Database Systems

The field of data mining and database systems is moving towards developing more efficient and privacy-preserving methods for extracting valuable information from large datasets. Researchers are focusing on creating algorithms that can hide sensitive information while maintaining the utility of the data. Additionally, there is a growing interest in graph analytics and extracting user-intended graphs from relational databases. The development of new frameworks and methods for text-to-SQL generation is also a notable trend, with a focus on improving the accuracy and robustness of these systems. Noteworthy papers in this area include:

  • A paper that proposes two algorithms, MU-MAP and MU-MIP, for privacy-preserving utility mining, which achieve an Artificial Cost value of 0 on all datasets.
  • A paper that introduces the SteinerSQL framework, which unifies mathematical reasoning and schema navigation for text-to-SQL generation, achieving state-of-the-art results on challenging benchmarks.

Sources

Utility-based Privacy Preserving Data Mining

Discovering Top-k Periodic and High-Utility Patterns

ExtGraph: A Fast Extraction Method of User-intended Graphs from a Relational Database

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases

Gamma Acyclicity, Annotated Relations, and Consistency Witness Functions

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation

Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs

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