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Measuring big data variety using Kol...

ProQuest Information and Learning Co.

Measuring big data variety using Kolmogorov's Complexity.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Measuring big data variety using Kolmogorov's Complexity./
作者:	Whetsel, Robert C.
面頁冊數:	1 online resource (138 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339589237

Measuring big data variety using Kolmogorov's Complexity.
Whetsel, Robert C.

Measuring big data variety using Kolmogorov's Complexity. - 1 online resource (138 pages)

Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.

Thesis (D.C.S.)--Colorado Technical University, 2016.

Includes bibliographical references

The current way to describe big data is by its volume, velocity, and variety. While volume and velocity can be measured, variety is descriptive with no standard units of measurement, hindering accurate estimation of compute resources. This research is proposed to extend Kolmogorov's Complexity Theory for generating an approximation of complexity as a unit of measurement for variety. The proposed approach was based on the recognition that data variety essentially poses a complexity problem and that a big dataset reduced to the bit level is nothing more than a concatenation of bit strings. A developed methodology based on relationships within a big dataset was tested on big datasets. Results showed by defining the variety of a big dataset D as the complexity of several sub-datasets classified by a set of relationships R, then the variety of a big dataset could be measured based on the complexity of each relationship, ultimately aiding estimation of compute resources required to analyze that big dataset.

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

Mode of access: World Wide Web

ISBN: 9781339589237Subjects--Topical Terms:

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

554714
Electronic books.

Measuring big data variety using Kolmogorov's Complexity.
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500 $a Source: Dissertation Abstracts International, Volume: 77-08(E), Section: B.
500 $a Adviser: Yanzhen Qu.
502 $a Thesis (D.C.S.)--Colorado Technical University, 2016.
504 $a Includes bibliographical references
520 $a The current way to describe big data is by its volume, velocity, and variety. While volume and velocity can be measured, variety is descriptive with no standard units of measurement, hindering accurate estimation of compute resources. This research is proposed to extend Kolmogorov's Complexity Theory for generating an approximation of complexity as a unit of measurement for variety. The proposed approach was based on the recognition that data variety essentially poses a complexity problem and that a big dataset reduced to the bit level is nothing more than a concatenation of bit strings. A developed methodology based on relationships within a big dataset was tested on big datasets. Results showed by defining the variety of a big dataset D as the complexity of several sub-datasets classified by a set of relationships R, then the variety of a big dataset could be measured based on the complexity of each relationship, ultimately aiding estimation of compute resources required to analyze that big dataset.
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
650 4 $a Information science. $3 561178
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a Colorado Technical University. $b Information Technology. $3 1185287
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