語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Towards analytical techniques for op...
~
Kreinovich, Vladik.
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big databy L. Octavio Lerma, Vladik Kreinovich.
作者:
Lerma, L. Octavio.
其他作者:
Kreinovich, Vladik.
出版者:
Cham :Springer International Publishing :2018.
面頁冊數:
viii, 141 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Electronic data processingDistributed processing.
電子資源:
http://dx.doi.org/10.1007/978-3-319-61349-9
ISBN:
9783319613499$q(electronic bk.)
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
Lerma, L. Octavio.
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
[electronic resource] /by L. Octavio Lerma, Vladik Kreinovich. - Cham :Springer International Publishing :2018. - viii, 141 p. :ill., digital ;24 cm. - Studies in big data,v.292197-6503 ;. - Studies in big data ;v.1..
Introduction -- Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing -- Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data--we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.
ISBN: 9783319613499$q(electronic bk.)
Standard No.: 10.1007/978-3-319-61349-9doiSubjects--Topical Terms:
182427
Electronic data processing
--Distributed processing.
LC Class. No.: QA76.9.D5
Dewey Class. No.: 004.6
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
LDR
:02301nmm a2200325 a 4500
001
527877
003
DE-He213
005
20170829141704.0
006
m d
007
cr nn 008maaau
008
181022s2018 gw s 0 eng d
020
$a
9783319613499$q(electronic bk.)
020
$a
9783319613482$q(paper)
024
7
$a
10.1007/978-3-319-61349-9
$2
doi
035
$a
978-3-319-61349-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.D5
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
004.6
$2
23
090
$a
QA76.9.D5
$b
L616 2018
100
1
$a
Lerma, L. Octavio.
$3
799920
245
1 0
$a
Towards analytical techniques for optimizing knowledge acquisition, processing, propagation, and use in cyberinfrastructure and big data
$h
[electronic resource] /
$c
by L. Octavio Lerma, Vladik Kreinovich.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2018.
300
$a
viii, 141 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Studies in big data,
$x
2197-6503 ;
$v
v.29
505
0
$a
Introduction -- Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing -- Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
520
$a
This book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data--we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.
650
0
$a
Electronic data processing
$x
Distributed processing.
$3
182427
650
0
$a
Big data.
$3
609582
650
1 4
$a
Engineering.
$3
210888
650
2 4
$a
Computational Intelligence.
$3
338479
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
275288
650
2 4
$a
Big Data.
$3
760530
650
2 4
$a
Big Data/Analytics.
$3
742047
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
252959
700
1
$a
Kreinovich, Vladik.
$3
610822
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Studies in big data ;
$v
v.1.
$3
675357
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-61349-9
950
$a
Engineering (Springer-11647)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000149661
電子館藏
1圖書
電子書
EB QA76.9.D5 L616 2018 2018
一般使用(Normal)
預約保留
0
預約
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-3-319-61349-9
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入