Science & Technology Review
|
2020, 38(3): 47-67
• Exclusive: Big data strategy •
On big data algebra: A formal analytic methodology for big data science and engineering
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WANG Yingxu1,2,3, JIN Jin2
Affiliations
1. National Engineering Key Lab for Big Data System Software, School of Software, Tsinghua University, Beijing 100084, China;
2. Beijing National Research Center of Information Science and Technology, Tsinghua University, Beijing 100084, China;
3. International Institute of Cognitive Informatics and Cognitive Computing(ICIC), Department of Electrical and Computer Engineering, Schulich School of Engineering and Hotchkiss Brain Institute, University of Calgary, Calgary T2N 1N4, Canada
Published: 2020-02-13
doi: 10.3981/j.issn.1000-7857.2020.03.003
Outline
Basic researches of big data science have triggered the emergence of mathematical theories of big data systems. This paper presents a rigorous analytic methodology for big data science and engineering known as Big Data Algebra (BDA). The mathematical models of big data science in BDA are formally elicited from common patterns and essences of a wide variety of big data systems. BDA reveals that any big data system is a Recursively Typed Hyperstructure (RTHS) beyond the traditional domain of pure numbers. It leads to a set of algebraic operators for big data modeling, analysis, and synthesis towards the denotational mathematical structure of BDA. The formal principles and properties of big data and their mathematical manipulations provide a theoretical framework of big data science as the basis for applications in big data engineering.
big data
/
mathematical model
/
hyper-structure
/
algebra
/
algorithms
WANG Yingxu, 靳瑾.
论大数据代数(BDA):大数据科学与工程的分析方法.
科技导报,
2020
, 38
(3)
: 47
-67
.
DOI: 10.3981/j.issn.1000-7857.2020.03.003
WANG Yingxu, JIN Jin.
On big data algebra: A formal analytic methodology for big data science and engineering[J].
Science & Technology Review,
2020
, 38
(3)
: 47
-67
.
DOI: 10.3981/j.issn.1000-7857.2020.03.003
Year 2020 volume 38 Issue 3
PDF
565
167
Cite this Article
BibTeX
Article Info
doi: 10.3981/j.issn.1000-7857.2020.03.003
- Receive Date:2019-11-09
- Online Date:2020-04-01
- Published:2020-02-13