我在Applied Sciences（综合性、交叉性期刊，CiteScore=3.70；IF=2.84）组织了一个Special Issue，大题目是“大数据分析进展”，比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎（但不限于）下面四类稿件：（1）数据分析中的基础理论分析，例如一个系统的可预测性（比如时间序列的可预测性）、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析；（2）数据分析的新方法，例如挖掘因果关系的新方法（这和Topic 1也是相关的）、多模态分析的新方法、隐私计算的新方法等等；（3）推出新的、高价值的数据集、数据分析平台、数据分析工具等等；（4）把大数据分析的方法用到自然科学和社会科学的各个分支（并获得洞见），我们特别喜欢用到那些原来定量化程度不高的学科。
投稿链接：https://www.mdpi.com/journal/applsci/special_issues/75Y7F7607U
投稿截止时期延长到2023年12月31日，我们处理稿件非常快，欢迎大家投稿支持。
其中第二十篇论文已经正式发表：
Mobile emergency services are better able to meet the needs of frequent public emergencies; however, their data quality problems seriously affect decision-making. In order to reduce the interference of low-quality data and solve the problem of data quality ambiguity, this paper first summarizes the five characteristics of mobile emergency big data. Second, based on the characteristics of mobile emergency big data, four data quality dimensions are defined with reference to existing research and national standards and combined with the measure of medium truth degree to give single-dimension and multi-dimension data quality truth degree measure models. Finally, a subjective-objective, qualitative-quantitative mobile emergency big data quality evaluation method based on the measure of medium truth degree is formed. The validity and practicality of the method are also verified by examples of algorithmic analysis of fire text datasets from Australian mountain fire data and the Chinese Emergency Incident Corpus. The experiments show that the method can realize quantitative mobile emergency big data quality assessment, solve the problem of data quality ambiguity, and reduce the interference of low-quality data, so as to save resources and improve the analysis and decision-making ability.
https://www.mdpi.com/2076-3417/13/16/9072
