计算机科学
星型模式
信息架构
模式(遗传算法)
数据库架构
模式迁移
文件结构说明
情报检索
数据挖掘
半结构化模型
模式演化
数据库
数据库设计
万维网
XML
作者
Enrico Gallinucci,Matteo Golfarelli,Stefano Rizzi
标识
DOI:10.1016/j.is.2018.02.007
摘要
In document-oriented databases, schema is a soft concept and the documents in a collection can be stored using different local schemata. This gives designers and implementers augmented flexibility; however, it requires an extra effort to understand the rules that drove the use of alternative schemata when sets of documents with different —and possibly conflicting— schemata are to be analyzed or integrated. In this paper we propose a technique, called schema profiling, to explain the schema variants within a collection in document-oriented databases by capturing the hidden rules explaining the use of these variants. We express these rules in the form of a decision tree (schema profile). Consistently with the requirements we elicited from real users, we aim at creating explicative, precise, and concise schema profiles. The algorithm we adopt to this end is inspired by the well-known C4.5 classification algorithm and builds on two original features: the coupling of value-based and schema-based conditions within schema profiles, and the introduction of a novel measure of entropy to assess the quality of a schema profile. A set of experimental tests made on both synthetic and real datasets demonstrates the effectiveness and efficiency of our approach.
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