@inproceedings{
 title = {Streamflow - Programming model for data streaming in scientific workflows},
 type = {inproceedings},
 year = {2010},
 id = {a5f3e997-9262-3633-b1b0-7d57c8980e4c},
 created = {2019-10-01T17:21:00.054Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:45.506Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Herath2010},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 abstract = {Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists' behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows. © 2010 IEEE.},
 bibtype = {inproceedings},
 author = {Herath, C. and Plale, B.},
 doi = {10.1109/CCGRID.2010.116},
 booktitle = {CCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing}
}

{"_id":"x8WDLJPJreKCzN3ab","bibbaseid":"herath-plale-streamflowprogrammingmodelfordatastreaminginscientificworkflows-2010","downloads":0,"creationDate":"2018-03-12T19:10:27.486Z","title":"Streamflow - Programming model for data streaming in scientific workflows","author_short":["Herath, C.","Plale, B."],"year":2010,"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/42d295c0-0737-38d6-8b43-508cab6ea85d","bibdata":{"title":"Streamflow - Programming model for data streaming in scientific workflows","type":"inproceedings","year":"2010","id":"a5f3e997-9262-3633-b1b0-7d57c8980e4c","created":"2019-10-01T17:21:00.054Z","file_attached":false,"profile_id":"42d295c0-0737-38d6-8b43-508cab6ea85d","last_modified":"2019-10-01T17:23:45.506Z","read":"true","starred":false,"authored":"true","confirmed":"true","hidden":false,"citation_key":"Herath2010","folder_uuids":"73f994b4-a3be-4035-a6dd-3802077ce863","private_publication":false,"abstract":"Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists' behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows. © 2010 IEEE.","bibtype":"inproceedings","author":"Herath, C. and Plale, B.","doi":"10.1109/CCGRID.2010.116","booktitle":"CCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing","bibtex":"@inproceedings{\n title = {Streamflow - Programming model for data streaming in scientific workflows},\n type = {inproceedings},\n year = {2010},\n id = {a5f3e997-9262-3633-b1b0-7d57c8980e4c},\n created = {2019-10-01T17:21:00.054Z},\n file_attached = {false},\n profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},\n last_modified = {2019-10-01T17:23:45.506Z},\n read = {true},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n citation_key = {Herath2010},\n folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},\n private_publication = {false},\n abstract = {Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists' behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows. © 2010 IEEE.},\n bibtype = {inproceedings},\n author = {Herath, C. and Plale, B.},\n doi = {10.1109/CCGRID.2010.116},\n booktitle = {CCGrid 2010 - 10th IEEE/ACM International Conference on Cluster, Cloud, and Grid Computing}\n}","author_short":["Herath, C.","Plale, B."],"biburl":"https://bibbase.org/service/mendeley/42d295c0-0737-38d6-8b43-508cab6ea85d","bibbaseid":"herath-plale-streamflowprogrammingmodelfordatastreaminginscientificworkflows-2010","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["streamflow","programming","model","data","streaming","scientific","workflows","herath","plale"],"keywords":[],"authorIDs":[],"dataSources":["zgahneP4uAjKbudrQ","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}