{"title":"A Survey on Association Rule Mining for Enterprise Architecture Model Discovery","authors":"","doi":"10.1007/s12599-023-00844-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.</p>","PeriodicalId":55296,"journal":{"name":"Business & Information Systems Engineering","volume":"2 1","pages":""},"PeriodicalIF":7.9000,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Business & Information Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12599-023-00844-5","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0
Abstract
Association Rule Mining (ARM) is a field of data mining (DM) that attempts to identify correlations among database items. It has been applied in various domains to discover patterns, provide insight into different topics, and build understandable, descriptive, and predictive models. On the one hand, Enterprise Architecture (EA) is a coherent set of principles, methods, and models suitable for designing organizational structures. It uses viewpoints derived from EA models to express different concerns about a company and its IT landscape, such as organizational hierarchies, processes, services, applications, and data. EA mining is the use of DM techniques to obtain EA models. This paper presents a literature review to identify the newest and most cited ARM algorithms and techniques suitable for EA mining that focus on automating the creation of EA models from existent data in application systems and services. It systematically identifies and maps fourteen candidate algorithms into four categories useful for EA mining: (i) General Frequent Pattern Mining, (ii) High Utility Pattern Mining, (iii) Parallel Pattern Mining, and (iv) Distribute Pattern Mining. Based on that, it discusses some possibilities and presents an exemplification with a prototype hypothesizing an ARM application for EA mining.
摘要 关联规则挖掘（ARM）是数据挖掘（DM）的一个领域，它试图找出数据库项目之间的关联。它已被应用于各种领域，以发现模式，提供对不同主题的洞察力，并建立可理解的、描述性的和预测性的模型。一方面，企业架构（EA）是一套连贯的原则、方法和模型，适用于设计组织结构。一方面，企业架构（EA）是一套适用于设计组织结构的连贯的原则、方法和模型，它使用从 EA 模型中得出的观点来表达公司及其 IT 环境的不同关注点，如组织层次、流程、服务、应用和数据。EA 挖掘是使用 DM 技术获取 EA 模型。本文通过文献综述来确定适用于 EA 挖掘的最新和最常被引用的 ARM 算法和技术，这些算法和技术侧重于从应用系统和服务中的现有数据自动创建 EA 模型。它系统地识别了十四种候选算法，并将其映射为四类对 EA 挖掘有用的算法：(i) 通用频繁模式挖掘，(ii) 高实用性模式挖掘，(iii) 并行模式挖掘，以及 (iv) 分布模式挖掘。在此基础上，它讨论了一些可能性，并提出了一个假设 ARM 应用于 EA 挖掘的原型示例。
期刊介绍：
BISE (Business & Information Systems Engineering) is an international scholarly journal that undergoes double-blind peer review. It publishes scientific research on the effective and efficient design and utilization of information systems by individuals, groups, enterprises, and society to enhance social welfare. Information systems are viewed as socio-technical systems involving tasks, people, and technology. Research in the journal addresses issues in the analysis, design, implementation, and management of information systems.
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