東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Big Data analytics in static and str...

Chen, Peng.

Linked to FindBook

Google Book

Amazon

博客來

Big Data analytics in static and streaming provenance.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Big Data analytics in static and streaming provenance./
Author:	Chen, Peng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	191 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
Contained By:	Dissertation Abstracts International77-09B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10103287
ISBN:	9781339668703

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

Big Data analytics in static and streaming provenance. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 191 p.

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

Thesis (Ph.D.)--Indiana University, 2016.

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.

ISBN: 9781339668703Subjects--Topical Terms:

523869
Computer science.

Big Data analytics in static and streaming provenance.
LDR:02504nmm a2200301 4500 001 2154550
005 20180419104821.5
008 190424s2016 ||||||||||||||||| ||eng d
020 $a 9781339668703
035 $a (MiAaPQ)AAI10103287
035 $a (MiAaPQ)indiana:14031
035 $a AAI10103287
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chen, Peng. $3 1910191
245 1 0 $a Big Data analytics in static and streaming provenance.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 191 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-09(E), Section: B.
500 $a Adviser: Beth A. Plale.
502 $a Thesis (Ph.D.)--Indiana University, 2016.
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.
590 $a School code: 0093.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a Indiana University. $b Computer Sciences. $3 1018516
773 0 $t Dissertation Abstracts International $g 77-09B(E).
790 $a 0093
791 $a Ph.D.
792 $a 2016
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10103287