Scientific Programming

Programming Foundations for Scientific Big Data Analytics


Status
Published

Lead Editor

1Cleveland State University, Cleveland, USA

2Deakin University, Burwood, Australia

3Central South University, Changsha, China


Programming Foundations for Scientific Big Data Analytics

Description

Big data analytics is the process of examining large data sets to uncover hidden patterns and previously unknown correlations. Big data analytics has been widely used in businesses to find market trends, customer preferences, and other useful business information. The research community is also beginning to embrace this exciting and powerful technology. Considering the huge amount of data produced in scientific fields such as biology, medicine, physics, and material science, big data analytics can be a powerful means of making new scientific discoveries. Efficient and effective big data analytics requires the development of programming tools and models.

In this special issue, we invite original research articles as well as review articles on the research and development of programing foundations for scientific big data analytics.

Potential topics include but are not limited to the following:

  • Programming models and environments for big data
  • Visual programming approach to big data analytics
  • Programming frameworks for big data in cloud computing environment
  • Big data challenges in dynamic program analysis
  • Program analysis for secure big data processing
  • Big data challenges: A program optimization perspective
  • Distributed programming in big data classification
  • Developing programming tools and models for scientific big data processing

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 2707604
  • - Editorial

Programming Foundations for Scientific Big Data Analytics

Wenbing Zhao | Longxiang Gao | Anfeng Liu
  • Special Issue
  • - Volume 2018
  • - Article ID 4350183
  • - Research Article

Analysis of Behavioral Economics in Crowdsensing: A Loss Aversion Cooperation Model

Deng Li | Liying Qiu | ... | Congwen Xiao
  • Special Issue
  • - Volume 2018
  • - Article ID 9308742
  • - Research Article

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Xu Xia | Zhigang Chen | Wei Wei
  • Special Issue
  • - Volume 2018
  • - Article ID 3732120
  • - Research Article

An Incremental Optimal Weight Learning Machine of Single-Layer Neural Networks

Hai-Feng Ke | Cheng-Bo Lu | ... | Xue-Wen Shen
  • Special Issue
  • - Volume 2018
  • - Article ID 2943290
  • - Research Article

Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

Xun Pu | ShanXiong Chen | ... | Le Zhang
  • Special Issue
  • - Volume 2018
  • - Article ID 4563040
  • - Research Article

Robust Matching Pursuit Extreme Learning Machines

Zejian Yuan | Xin Wang | ... | Badong Chen
  • Special Issue
  • - Volume 2018
  • - Article ID 1327214
  • - Review Article

Big Data Management for Cloud-Enabled Geological Information Services

Yueqin Zhu | Yongjie Tan | ... | Zhijie He
  • Special Issue
  • - Volume 2018
  • - Article ID 6749561
  • - Research Article

Incremental Graph Pattern Matching Algorithm for Big Graph Data

Lixia Zhang | Jianliang Gao
  • Special Issue
  • - Volume 2017
  • - Article ID 3704525
  • - Research Article

Adaptive Ensemble Method Based on Spatial Characteristics for Classifying Imbalanced Data

Lei Wang | Lei Zhao | ... | Ruochen Huang
  • Special Issue
  • - Volume 2017
  • - Article ID 2056501
  • - Research Article

Routing Optimization Algorithms Based on Node Compression in Big Data Environment

Lifeng Yang | Liangming Chen | ... | Zhifang Liao
Scientific Programming
 Journal metrics
Acceptance rate27%
Submission to final decision65 days
Acceptance to publication33 days
CiteScore2.300
Impact Factor0.963
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