國立虎尾科技大學 |

Improving Usability and Scalability of Big Data Workflows in the Cloud.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving Usability and Scalability of Big Data Workflows in the Cloud./
作者:	Mohan, Aravind.
面頁冊數:	1 online resource (130 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Contained By:	Dissertation Abstracts International79-01B(E).
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355221886

Improving Usability and Scalability of Big Data Workflows in the Cloud.
Mohan, Aravind.

Improving Usability and Scalability of Big Data Workflows in the Cloud. - 1 online resource (130 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Wayne State University, 2017.

Includes bibliographical references

Big data workflows have recently emerged as the next generation of data-centric workflow technologies to address the five "V" challenges of big data: volume, variety, velocity, veracity, and value. More formally, a big data workflow is the computerized modeling and automation of a process consisting of a set of computational tasks and their data interdependencies to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. The convergence of big data and workflows creates new challenges in workflow community.

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

Mode of access: World Wide Web

ISBN: 9780355221886Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Usability and Scalability of Big Data Workflows in the Cloud.
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Advisers: Shiyong Lu; Jiang Song.
502 $a Thesis (Ph.D.)--Wayne State University, 2017.
504 $a Includes bibliographical references
520 $a Big data workflows have recently emerged as the next generation of data-centric workflow technologies to address the five "V" challenges of big data: volume, variety, velocity, veracity, and value. More formally, a big data workflow is the computerized modeling and automation of a process consisting of a set of computational tasks and their data interdependencies to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. The convergence of big data and workflows creates new challenges in workflow community.
520 $a First, the variety of big data results in a need for integrating large number of remote Web services and other heterogeneous task components that can consume and produce data in various formats and models into a uniform and interoperable workflow. Existing approaches fall short in addressing the so-called shimming problem only in an adhoc manner and unable to provide a generic solution. We automatically insert a piece of code called shims or adaptors in order to resolve the data type mismatches.
520 $a Second, the volume of big data results in a large number of datasets that needs to be queried and analyzed in an effective and personalized manner. Further, there is also a strong need for sharing, reusing, and repurposing existing tasks and workflows across different users and institutes. To overcome such limitations, we propose a folksonomy-based social workflow recommendation system to improve workflow design productivity and efficient dataset querying and analyzing.
520 $a Third, the volume of big data results in the need to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. But a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. We propose a NoSQL collectional data model that addresses this limitation.
520 $a Finally, the volume of big data combined with the unbound resource leasing capability foreseen in the cloud, facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. We propose BARENTS scheduler that supports high-performance workflow scheduling in a heterogeneous cloud-computing environment with a single objective to minimize the workflow makespan under a user provided budget constraint.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a Wayne State University. $b Computer Engineering. $3 1195522
773 0 $t Dissertation Abstracts International $g 79-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10620485 $z click for full text (PQDT)