Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces
Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces
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DOI：
10.1145/3297662.3365822
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
H. Ishikawa;Daiju Kato;Masaki Endo;Masaharu Hirota
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文献类型：
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作者：
H. Ishikawa;Daiju Kato;Masaki Endo;Masaharu Hirota
对于涉及不同来源的社会大数据的应用，生成综合假设的分析方法是必要的。在本文中，我们首先引入了一个抽象的数据模型，通过使用家庭，集合集合的数学概念来集成数据管理和数据挖掘，以促进社会大数据应用所需的可重复性和问责性。接下来，我们描述广义差分方法作为一种生成综合假设的方法。最后，我们通过将我们的数据模型作为广义差分方法指导下的描述，将它们应用于涉及概念空间和实际空间数据的三个用例来验证我们的建议。
Analytic methodology as to generation of integrated hypotheses is necessary for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we describe generalized difference methods as a methodology for generating integrated hypotheses. Finally, we validate our proposal by applying them to three use cases involving data in concept and real spaces by using our data model as their description guided by generalized difference methods.