Ontology-based workflow pattern mining: application to bioinformatics expertise acquisition

Ahmed Halioui, Tomas Martin, Petko Valtchev, Abdoulaye Baniré Diallo
{"title":"Ontology-based workflow pattern mining: application to bioinformatics expertise acquisition","authors":"Ahmed Halioui, Tomas Martin, Petko Valtchev, Abdoulaye Baniré Diallo","doi":"10.1145/3019612.3019866","DOIUrl":null,"url":null,"abstract":"Workflow platforms enable the construction of solutions to complex problems as step-wise processes made of components including methods, tools, data formats, parameters, etc. Successful workflow solutions require a mastering of the different components paving the way to automated acquisition of problem solving expertise. Thus, process mining could be applied to discover workflow patterns. Due to the combinatorics of component instances in rich domains such as bioinformatics, generalized patterns could be a relevant way of abstraction. Here, we propose an approach for mining workflow patterns, defined on the top of a domain ontology which categorizes workflow elements and their interactions. While original workflows are doubly-labelled DAGs, the underlying problem is transformed into a mining of generalized sequential patterns with links between their items. The proposed mining method traverses the ensuing pattern space using five refinement primitives that exploit the is-a links from the ontology. To assess the prediction power of the approach, we applied the generated patterns as templates in a recommendation platform to complete partial workflows under construction. The analyses of recommendations vs. actual content of a real-world dataset reveals that non trivial patterns can be found and further used to provide plausible recommendations with high accuracies (fMeasure >75+).","PeriodicalId":20728,"journal":{"name":"Proceedings of the Symposium on Applied Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Symposium on Applied Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3019612.3019866","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Workflow platforms enable the construction of solutions to complex problems as step-wise processes made of components including methods, tools, data formats, parameters, etc. Successful workflow solutions require a mastering of the different components paving the way to automated acquisition of problem solving expertise. Thus, process mining could be applied to discover workflow patterns. Due to the combinatorics of component instances in rich domains such as bioinformatics, generalized patterns could be a relevant way of abstraction. Here, we propose an approach for mining workflow patterns, defined on the top of a domain ontology which categorizes workflow elements and their interactions. While original workflows are doubly-labelled DAGs, the underlying problem is transformed into a mining of generalized sequential patterns with links between their items. The proposed mining method traverses the ensuing pattern space using five refinement primitives that exploit the is-a links from the ontology. To assess the prediction power of the approach, we applied the generated patterns as templates in a recommendation platform to complete partial workflows under construction. The analyses of recommendations vs. actual content of a real-world dataset reveals that non trivial patterns can be found and further used to provide plausible recommendations with high accuracies (fMeasure >75+).
基于本体的工作流模式挖掘:在生物信息学专业知识获取中的应用
工作流平台可以将复杂问题的解决方案构建为由方法、工具、数据格式、参数等组成的逐步过程。成功的工作流解决方案需要掌握不同的组件,为自动获取解决问题的专业知识铺平道路。因此,流程挖掘可以应用于发现工作流模式。在生物信息学等丰富的领域中,由于组件实例的组合性,广义模式可能是一种相关的抽象方式。在这里,我们提出了一种挖掘工作流模式的方法,该模式定义在对工作流元素及其交互进行分类的领域本体之上。虽然原始工作流是双重标记的dag,但潜在的问题被转换为对具有项目之间链接的广义顺序模式的挖掘。所提出的挖掘方法使用利用本体中的is-a链接的五个改进原语遍历随后的模式空间。为了评估该方法的预测能力,我们将生成的模式作为模板应用于推荐平台中,以完成正在构建的部分工作流。对推荐与真实数据集的实际内容的分析表明,可以找到非平凡模式,并进一步用于提供具有高准确性的可信推荐(fMeasure >75+)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信