國立虎尾科技大學 |

Big Data analytics in static and streaming provenance.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Big Data analytics in static and streaming provenance./
作者:	Chen, Peng.
面頁冊數:	1 online resource (191 pages)
附註:	Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
Contained By:	Dissertation Abstracts International77-09B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781339668703

Big Data analytics in static and streaming provenance.
Chen, Peng.

Big Data analytics in static and streaming provenance. - 1 online resource (191 pages)

Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

With recent technological and computational advances, scientists increasingly integrate sensors and model simulations to understand spatial, temporal, social, and ecological relationships at unprecedented scale. Data provenance traces relationships of entities over time, thus providing a unique view on over-time behavior under study. However, provenance can be overwhelming in both volume and complexity; the now forecasting potential of provenance creates additional demands.

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

Mode of access: World Wide Web

ISBN: 9781339668703Subjects--Topical Terms:

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

554714
Electronic books.

Big Data analytics in static and streaming provenance.
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245 1 0 $a Big Data analytics in static and streaming provenance.
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500 $a Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
500 $a Adviser: Beth A. Plale.
502 $a Thesis (Ph.D.) $c Indiana University $d 2016.
504 $a Includes bibliographical references
520 $a With recent technological and computational advances, scientists increasingly integrate sensors and model simulations to understand spatial, temporal, social, and ecological relationships at unprecedented scale. Data provenance traces relationships of entities over time, thus providing a unique view on over-time behavior under study. However, provenance can be overwhelming in both volume and complexity; the now forecasting potential of provenance creates additional demands.
520 $a This dissertation focuses on Big Data analytics of static and streaming provenance. It develops filters and a non-preprocessing slicing technique for in-situ querying of static provenance. It presents a stream processing framework for online processing of provenance data at high receiving rate. While the former is sufficient for answering queries that are given prior to the application start (forward queries), the latter deals with queries whose targets are unknown beforehand (backward queries). Finally, it explores data mining on large collections of provenance and proposes a temporal representation of provenance that can reduce the high dimensionality while effectively supporting mining tasks like clustering, classification and association rules mining; and the temporal representation can be further applied to streaming provenance as well. The proposed techniques are verified through software prototypes applied to Big Data provenance captured from computer network data, weather models, ocean models, remote (satellite) imagery data, and agent-based simulations of agricultural decision making.
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
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710 2 $a Indiana University. $b Computer Sciences. $3 1179305
773 0 $t Dissertation Abstracts International $g 77-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10103287 $z click for full text (PQDT)