一句话：提出结合机器学习、知识表示与程序合成的数据迁移辅助工具，自动建议字段匹配和转换规则。
数据迁移是企业信息系统现代化的关键步骤，其核心是将源系统数据转换为目标系统数据，但转换规约的编写通常依赖专家且耗时。本文提出一种辅助工具：利用机器学习和知识表示在源、目标数据模型之间建议候选字段匹配，并利用程序合成生成数据转换规则；工具接收专家对匹配和已合成规则的反馈，进而迭代提出新的匹配与转换规则。作者在真实工业数据上评估该工具，schema matching 的 recall@5 为 0.76，规则生成的 recall@2 为 0.81。
Data migration from source to target information system is a critical step for modernizing information systems. Central to data migration is data transform that transforms the source system data into target system. In this paper we present a tool that assists the experts in creating the data transformation specification by (a) suggesting candidate field matches between the source and target data models using machine learning and knowledge representation, and (b) rules for the data transformation using program synthesis. It takes the expert’s feedback for the identified matches and synthesized rules and proposes new matches and transformation rules. We have executed our tool on real-life industrial data. Our schema matching recall at 5 is 0.76, while for the rule generator recall at 2 is 0.81.
DOI 原文 ·
@inproceedings{paperbot6528,
title = {Learning-based Assistant for Data Migration of Enterprise Information Systems},
author = {Sayandeep Mitra and Debayan Mukherjee and Atreya Bandyopadhyay and Rajdip Chowdhury and Raveendra Kumar Medicherla and Indrajit Bhattacharya and Ravindra Naik},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678533}
}