國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science and visual computing

Dill, John.

Data science and visual computing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science and visual computingby Rae Earnshaw, John Dill, David Kasik.
作者:	Earnshaw, Rae.
其他作者:	Dill, John.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xviii, 108 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Optical data processing.
電子資源:	https://doi.org/10.1007/978-3-030-24367-8
ISBN:	9783030243678$q(electronic bk.)

Data science and visual computing
Earnshaw, Rae.

Data science and visual computing[electronic resource] /by Rae Earnshaw, John Dill, David Kasik. - Cham :Springer International Publishing :2019. - xviii, 108 p. :ill., digital ;24 cm. - SpringerBriefs in advanced information and knowledge processing,2524-5198. - SpringerBriefs in advanced information and knowledge processing..

Data Science -- Big Data -- Visual Computing -- Visualization -- Geometric Visualization -- Visual Analytics -- Data Science Institutes and Data Centers.

Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.

ISBN: 9783030243678$q(electronic bk.)

Standard No.: 10.1007/978-3-030-24367-8doiSubjects--Topical Terms:

185609
Optical data processing.

LC Class. No.: TA1630 / .E37 2019

Dewey Class. No.: 621.367

Data science and visual computing
LDR:02806nmm a2200349 a 4500 001 586709
003 DE-He213
005 20200701092205.0
006 m d
007 cr nn 008maaau
008 210326s2019 sz s 0 eng d
020 $a 9783030243678$q(electronic bk.)
020 $a 9783030243661$q(paper)
024 7 $a 10.1007/978-3-030-24367-8 $2 doi
035 $a 978-3-030-24367-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1630 $b .E37 2019
072 7 $a UNF $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UKS $2 thema
082 0 4 $a 621.367 $2 23
090 $a TA1630 $b .E12 2019
100 1 $a Earnshaw, Rae. $3 591605
245 1 0 $a Data science and visual computing $h [electronic resource] / $c by Rae Earnshaw, John Dill, David Kasik.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xviii, 108 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in advanced information and knowledge processing, $x 2524-5198
505 0 $a Data Science -- Big Data -- Visual Computing -- Visualization -- Geometric Visualization -- Visual Analytics -- Data Science Institutes and Data Centers.
520 $a Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.
650 0 $a Optical data processing. $3 185609
650 0 $a Big data. $3 609582
650 1 4 $a Data Storage Representation. $3 277024
650 2 4 $a Computer Graphics. $3 274515
650 2 4 $a User Interfaces and Human Computer Interaction. $3 274517
700 1 $a Dill, John. $3 561558
700 1 $a Kasik, David. $3 878256
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in advanced information and knowledge processing. $3 825712
856 4 0 $u https://doi.org/10.1007/978-3-030-24367-8
950 $a Computer Science (SpringerNature-11645)