Journal of Function Spaces

Mathematical Modeling for Next-Generation Big Data Technologies


Publishing date
01 Mar 2023
Status
Published
Submission deadline
14 Oct 2022

Lead Editor

1Chaohu University, Hefei, China

2University of Central Missouri, Warrensburg, USA

3Sanjiang University, Nanjing, China

4Southeast University, Nanjing, China


Mathematical Modeling for Next-Generation Big Data Technologies

Description

Datafication and informatization are the current major trends in economic and social development. Big data technology has a wide range of applications in the fields of industry, finance, transportation, healthcare, etc. However, the means to make full use of data to build mathematical models of practical problems is a major task of mathematical modeling in the era of big data and brings challenges to traditional mathematical modeling. With the rise of big data and deep learning, data modeling has attracted increasing attention.

Although data can be descriptive of real-world problems, it only provides a partial description unless the given data can traverse every possible scenario. However, mathematical models can accurately reflect the essential characteristics of problems and can be applied for analysis, explanation, and prediction. Data modeling methods based on big data analysis can bypass the shortcomings of traditional modeling methods that rely solely on models and hypotheses. Therefore, the relationship between data and models can be complementary, and the combination of data modeling and manual modeling by machine learning is the direction that future mathematical modeling should take.

This Special Issue aims to bring together original research and review articles in the field of mathematical modeling for next-generation big data technologies in industry, finance, transportation, education, healthcare, etc. We highly encourage submissions discussing the methods of data modeling in the big data era. Submissions can also focus on engineering, management, transportation, education, applied mathematics, etc.

Potential topics include but are not limited to the following:

  • Mathematical models for machine learning
  • Mathematical modeling in optimization calculation of chaos model of big data
  • Optimization of medical service based on multi-objective programming
  • Application of mathematical modeling in workflow management
  • Mathematical modeling and simulation analysis based on feature data classification
  • Educational evaluation based on mathematical modeling
  • Analysis of transportation network model based on mathematical modeling
  • Mathematical modeling in engineering problems
  • Analysis of financial optimization model in mathematical modeling
  • Supply chain demand forecasting modeling based on big data
  • Network public opinion analysis model based on big data
Journal of Function Spaces
 Journal metrics
See full report
Acceptance rate12%
Submission to final decision115 days
Acceptance to publication20 days
CiteScore2.600
Journal Citation Indicator1.430
Impact Factor1.9
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