國立虎尾科技大學 |

Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling./
作者:	Ruan, Dong.
面頁冊數:	1 online resource (120 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355826722

Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling.
Ruan, Dong.

Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling. - 1 online resource (120 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Wayne State University, 2018.

Includes bibliographical references

With the development of e-Science, scientific workflow has been widely used by scientists to perform complicated experiments and get important scientific discoveries. Due to the nature of science, scientific workflow often involves complex workflow design and distributed computation resources, so abnormal events are likely to happen and interrupt the normal execution of workflows. Thus, workflow monitoring and exception handling play a significant role within the context of scientific workflow. Machine learning pipelines are data pipelines which implement the tasks required during the machine learning application development. Scientific workflow could bring unique advantages when building machine learning pipelines.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355826722Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling.
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245 1 0 $a Models, Languages, and Algorithms for Scientific Workflow Monitoring and Exception Handling.
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300 $a 1 online resource (120 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Shiyong Lu.
502 $a Thesis (Ph.D.)--Wayne State University, 2018.
504 $a Includes bibliographical references
520 $a With the development of e-Science, scientific workflow has been widely used by scientists to perform complicated experiments and get important scientific discoveries. Due to the nature of science, scientific workflow often involves complex workflow design and distributed computation resources, so abnormal events are likely to happen and interrupt the normal execution of workflows. Thus, workflow monitoring and exception handling play a significant role within the context of scientific workflow. Machine learning pipelines are data pipelines which implement the tasks required during the machine learning application development. Scientific workflow could bring unique advantages when building machine learning pipelines.
520 $a In this dissertation, to tackle the challenges of workflow monitoring and exception handling, we propose a scientific workflow monitoring model and several workflow monitoring algorithms to realize efficient and effective workflow monitoring. We also propose architecture for workflow monitoring in DATAVIEW. Then we propose a user-defined exception handling framework for DATAVIEW, including a scientific workflow exception handling language, two exception handling algorithms as well as the exception handling architecture in DATAVIEW. At last, we showcase a case study using DATAVIEW to analyze NYC Citi Bike data by building machine learning pipelines using scientific workflows.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Wayne State University. $b Computer Science. $3 1182091
773 0 $t Dissertation Abstracts International $g 79-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10641351 $z click for full text (PQDT)