{"title":"Geoweaver_cwl:将geoweaver AI工作流转换为通用工作流语言,以扩展互操作性","authors":"Amruta Kale , Ziheng Sun , Chao Fan , Xiaogang Ma","doi":"10.1016/j.acags.2023.100126","DOIUrl":null,"url":null,"abstract":"<div><p>Recently, workflow management platforms are gaining more attention in the artificial intelligence (AI) community. Traditionally, researchers self-managed their workflows in a manual and tedious way that heavily relies on their memory. Due to the complexity and unpredictability of AI models, they often struggled to track and manage all the data, steps, and history of the workflow. AI workflows are time-consuming, redundant, and error-prone, especially when big data is involved. A common strategy to make these workflows more manageable is to use a workflow management system, and we recommend Geoweaver, an open-source workflow management system that enables users to create, modify and reuse AI workflows all in one place. To make our work in Geoweaver reusable by the other workflow management systems, we created an add-on functionality <strong><em>geoweaver_cwl</em></strong>, a Python package that automatically converts Geoweaver AI workflows into the Common Workflow Language (CWL) format. It will allow researchers to easily share, exchange, modify, reuse, and build a new workflow from existing ones in other CWL-compliant software. A user study was conducted with the existing workflows created by Geoweaver to collect suggestions and fill in the gaps between our package and Geoweaver. The evaluation confirms that <strong><em>geoweaver_cwl</em></strong> can lead to a well-versed AI process while disclosing opportunities for further extensions. The <strong><em>geoweaver_cwl</em></strong> package is publicly released online at <span>https://pypi.org/project/geoweaver-cwl/0.0.1/</span><svg><path></path></svg>.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"19 ","pages":"Article 100126"},"PeriodicalIF":2.6000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow language to extend interoperability\",\"authors\":\"Amruta Kale , Ziheng Sun , Chao Fan , Xiaogang Ma\",\"doi\":\"10.1016/j.acags.2023.100126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Recently, workflow management platforms are gaining more attention in the artificial intelligence (AI) community. Traditionally, researchers self-managed their workflows in a manual and tedious way that heavily relies on their memory. Due to the complexity and unpredictability of AI models, they often struggled to track and manage all the data, steps, and history of the workflow. AI workflows are time-consuming, redundant, and error-prone, especially when big data is involved. A common strategy to make these workflows more manageable is to use a workflow management system, and we recommend Geoweaver, an open-source workflow management system that enables users to create, modify and reuse AI workflows all in one place. To make our work in Geoweaver reusable by the other workflow management systems, we created an add-on functionality <strong><em>geoweaver_cwl</em></strong>, a Python package that automatically converts Geoweaver AI workflows into the Common Workflow Language (CWL) format. It will allow researchers to easily share, exchange, modify, reuse, and build a new workflow from existing ones in other CWL-compliant software. A user study was conducted with the existing workflows created by Geoweaver to collect suggestions and fill in the gaps between our package and Geoweaver. The evaluation confirms that <strong><em>geoweaver_cwl</em></strong> can lead to a well-versed AI process while disclosing opportunities for further extensions. The <strong><em>geoweaver_cwl</em></strong> package is publicly released online at <span>https://pypi.org/project/geoweaver-cwl/0.0.1/</span><svg><path></path></svg>.</p></div>\",\"PeriodicalId\":33804,\"journal\":{\"name\":\"Applied Computing and Geosciences\",\"volume\":\"19 \",\"pages\":\"Article 100126\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing and Geosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590197423000150\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing and Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590197423000150","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Geoweaver_cwl: Transforming geoweaver AI workflows to common workflow language to extend interoperability
Recently, workflow management platforms are gaining more attention in the artificial intelligence (AI) community. Traditionally, researchers self-managed their workflows in a manual and tedious way that heavily relies on their memory. Due to the complexity and unpredictability of AI models, they often struggled to track and manage all the data, steps, and history of the workflow. AI workflows are time-consuming, redundant, and error-prone, especially when big data is involved. A common strategy to make these workflows more manageable is to use a workflow management system, and we recommend Geoweaver, an open-source workflow management system that enables users to create, modify and reuse AI workflows all in one place. To make our work in Geoweaver reusable by the other workflow management systems, we created an add-on functionality geoweaver_cwl, a Python package that automatically converts Geoweaver AI workflows into the Common Workflow Language (CWL) format. It will allow researchers to easily share, exchange, modify, reuse, and build a new workflow from existing ones in other CWL-compliant software. A user study was conducted with the existing workflows created by Geoweaver to collect suggestions and fill in the gaps between our package and Geoweaver. The evaluation confirms that geoweaver_cwl can lead to a well-versed AI process while disclosing opportunities for further extensions. The geoweaver_cwl package is publicly released online at https://pypi.org/project/geoweaver-cwl/0.0.1/.