Automatic Recommendations for Evolving Relational Databases Schema

• URL: http://arxiv.org/abs/2404.08525v1
• Date: Fri, 12 Apr 2024 15:14:38 GMT
• Title: Automatic Recommendations for Evolving Relational Databases Schema
• Authors: Anne Etien, Nicolas Anquetil,
• Abstract summary: We present a meta-model that computes the impact of planned changes on the database schema. We show that without detailed knowledge of the database, we could perform the same change in 75% less time than the expert database architect.
• Score: 0.7412445894287709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational databases play a central role in many information systems. Their schema contains structural (e.g. tables and columns) and behavioral (e.g. stored procedures or views) entity descriptions. Then, just like for ``normal'' software, changes in legislation, offered functionalities, or functional contexts, impose to evolve databases and their schemas. But in some scenarios, it is not so easy to deconstruct a wished evolution of the schema into a precise sequence of operations. Changing a database schema may impose manually dropping and recreating dependent entities, or manually searching for dependencies in stored procedures. This is important because getting even the order of application of the operators can be difficult and have profound consequences. This meta-model allows us to compute the impact of planned changes and recommend additional changes that will ensure that the RDBMS constraints are always verified. The recommendations can then be compiled into a valid SQL patch actually updating the database schema in an orderly way. We replicated a past evolution showing that, without detailed knowledge of the database, we could perform the same change in 75\% less time than the expert database architect. We also exemplify the use of our approach on other planned changes.

Related papers

• Relational Database Augmented Large Language Model [59.38841050766026]
  Large language models (LLMs) excel in many natural language processing (NLP) tasks. They can only incorporate new knowledge through training or supervised fine-tuning processes. This precise, up-to-date, and private information is typically stored in relational databases.
  arXiv  Detail & Related papers  (2024-07-21T06:19:10Z)
• Compound Schema Registry [0.0]
  We propose the use of generalized schema evolution (GSE) facilitated by a compound AI system. This system employs Large Language Models (LLMs) to interpret the semantics of schema changes. Our approach includes developing a task-specific language, Transformation Language (STL), to generate schema mappings as an intermediate representation.
  arXiv  Detail & Related papers  (2024-06-17T05:50:46Z)
• PRICE: A Pretrained Model for Cross-Database Cardinality Estimation [78.30959470441442]
  Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high preparation costs. We propose PRICE, a PRetrained multI-table CardEst model, which addresses these limitations.
  arXiv  Detail & Related papers  (2024-06-03T06:21:53Z)
• DBCopilot: Scaling Natural Language Querying to Massive Databases [47.009638761948466]
  Existing methods face scalability challenges when dealing with massive, dynamically changing databases. This paper introduces DBCopilot, a framework that employs a compact and flexible copilot model for routing across massive databases.
  arXiv  Detail & Related papers  (2023-12-06T12:37:28Z)
• Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification [81.17473088621209]
  We treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs) We design an incremental prompting and verification method to break down the construction of a complex event graph into three stages. Compared to directly using LLMs to generate a linearized graph, our method can generate large and complex schemas with 7.2% F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical relations.
  arXiv  Detail & Related papers  (2023-07-05T01:00:44Z)
• Schema-adaptable Knowledge Graph Construction [47.772335354080795]
  Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. We propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training.
  arXiv  Detail & Related papers  (2023-05-15T15:06:20Z)
• AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language [0.0]
  We propose a semantic parsing approach to address the challenge of converting complex natural language into SQL. We modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production. To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
  arXiv  Detail & Related papers  (2022-10-16T13:31:32Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)
• BERT Meets Relational DB: Contextual Representations of Relational Databases [4.029818252558553]
  We address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables. We look into ways of using these attention-based model to learn embeddings for entities in the relational database.
  arXiv  Detail & Related papers  (2021-04-30T11:23:26Z)
• Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning [2.6464841907587004]
  We propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data. Our experimental results demonstrate that our proposed approach is effective for two real-world data integration scenarios.
  arXiv  Detail & Related papers  (2020-10-15T08:10:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.