[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-244516-105":53,"doc-detail-244516-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","detecting-common-scientific-workflow-fragments-using-templates-and-execution-provenance","Detecting Common Scientific Workflow Fragments Using Templates and Execution Provenance","","Provenance is essential for understanding and reusing methods in scientific experimentation because it records inputs, executed processes, and intermediate and final results. In in-silico workflows, systems such as Wings, Taverna, Galaxy, and Vistrails produce provenance, but it is often too fine-grained to interpret during execution. This paper proposes an approach to automatically derive higher-level abstractions from low-level provenance by detecting recurring workflow fragments and relating them to workflow templates.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/detecting-common-scientific-workflow-fragments-using-templates-and-execution-provenance/244516/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/detecting-common-scientific-workflow-fragments-using-templates-and-execution-provenance/244516.png","ImageObject",442,249,{"name":88,"@type":89},"Lucas Vance","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-21","2026-09-12",true,{"@type":98,"interactionType":99,"userInteractionCount":25},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why is provenance important in scientific workflows?","Question",{"text":108,"@type":109},"Provenance records inputs, the processes carried out, and how intermediate and final results are produced, enabling understanding and reuse of experiment methods.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What problem does the paper address regarding provenance data?",{"text":113,"@type":109},"Workflow provenance is often captured at too low a granularity, making it difficult to interpret what happened during execution, especially for users not involved in workflow development.",{"name":115,"@type":106,"acceptedAnswer":116},"How does the proposed approach improve the interpretation of workflow execution?",{"text":117,"@type":109},"It automatically identifies common workflow fragments on execution provenance and links them to workflow templates, producing higher-level abstractions.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},244516,1791744076,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":25,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":76},549768064622,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Detecting common scientific workflow fragments using templates and execution provenance  \nDaniel Garijo  \nOntology Engineering Group Depto. Inteligencia Artificial Facultad de Informática Universidad Politécnica de Madrid  \ndgarijo@fi. [upm.es](upm.es)  \nOscar Corcho  \nOntology Engineering Group Depto. Inteligencia Artificial Facultad de Informática Universidad Politécnica de Madrid  \nocorcho@fi. [upm.es](upm.es)  \nYolanda Gil  \nInformation Sciences Institute and Department of Computer Science University of Southern California  \n[gil@isi.edu](gil@isi.edu)  \nABSTRACT  \nProvenance plays a major role when understanding and reusing the methods applied in a scienti􀀌c experiment, as it provides a record of inputs, the processes carried out and the use and generation of intermediate and 􀀌nal results. In the speci􀀌c case of in-silico scienti􀀌c experiments, a large variety of scienti􀀌c work􀀍ow systems (e.g. , Wings, Taverna, Galaxy, Vistrails) have been created to support scientists. All of these systems produce some sort of provenance about the executions of the work􀀍ows that encode scienti􀀌c experiments. However, provenance is normally recorded at avery low level of detail, which complicates the understanding of what happened during execution. In this paper we propose an approach to automatically obtain abstractions from low-level provenance data by 􀀌nding common work􀀍ow fragments on work􀀍ow execution provenance and relating them to templates. We have tested our approach with a dataset of work􀀍ows published by the Wings work􀀍ow system. Our results show that by using these kinds of abstractions we can highlight the most common abstract methods used in the executions of a repository, relating di􀀋erent runs and work􀀍ow templates with each other.  \nCategories and Subject Descriptors I.2.6 [Learning]; I.2 [Arti􀀌cial Intelligence]  \nGeneral Terms  \nEXPERIMENTATION  \nKeywords  \nScienti􀀌c work􀀍ow, provenance, abstraction, Wings  \n1. INTRODUCTION  \nA scienti􀀌c work􀀍ow can be seen as a digital instrument that allows scientists to encode a scienti􀀌c experiment in the  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.  \nK-CAP '13, June 23-26, 2013, Banff, Canada.  \nCopyright 2013 ACM 978-1-45-03-2102-0/13/06 ...$15.00 .  \nform of a set of computational or data manipulation steps. Scienti􀀌c work􀀍ows play an important role in the reproducibility and replicability of scienti􀀌c experiments, as well as in repurposing and reusing results from previous experiments [13] . Given their importance in the research lifecycle, scienti􀀌c work􀀍ows are beginning to be included in scienti􀀌c publications, together with datasets and other elements used in the context of an experiment. At the same time, repositories of work􀀍ows like myExperiment [24], Crowdlabs [19] or Galaxy [10] facilitate work􀀍ow publication, exchange andreuse. These repositories currently store thousands 1 2 of work􀀍ows (referred to as work􀀍ow templates), which have been uploaded by scientists in many di􀀋erent domains (ranging from life sciences to text analytics or astronomy [8]) .  \nGiven that support for reproducibility and justi􀀌cation of the obtained results are some of the main roles of scienti􀀌c work􀀍ows, recording the provenance of work􀀍ow executions has become crucial to gain insight about what happened when executing a certain work􀀍ow, and consequently to explain what happened in a scienti􀀌c experiment. Hence work􀀍ow systems like Wings [11], Taverna [20], Vistrails [5] or Kepler [18] allow recording and exporting the provenance of work􀀍ow executions (according to di􀀋erent provenance models) along with their work􀀍ow templates.  \nThese work􀀍o","cbCaicvkafDFr9jq","https://ap.wps.com/l/cbCaicvkafDFr9jq","pdf",790869,8,"English","# Introduction\n## Provenance in scientific workflows\n## Motivation: low-granularity traces\n## Workflow motifs and abstractions\n## Automatic detection from provenance","[{\"question\":\"Why is provenance important in scientific workflows?\",\"answer\":\"Provenance records inputs, the processes carried out, and how intermediate and final results are produced, enabling understanding and reuse of experiment methods.\"},{\"question\":\"What problem does the paper address regarding provenance data?\",\"answer\":\"Workflow provenance is often captured at too low a granularity, making it difficult to interpret what happened during execution, especially for users not involved in workflow development.\"},{\"question\":\"How does the proposed approach improve the interpretation of workflow execution?\",\"answer\":\"It automatically identifies common workflow fragments on execution provenance and links them to workflow templates, producing higher-level abstractions.\"}]","Detecting Common Scientific Workflow Fragments Using Templates and Execution Provenance | PDF",1789218174]