Frequent Pattern Mining for Massive XBRL Data in Internet Information Disclosure System

被引：0

作者：

Feng, T. ^{[1
]}

Zeng, Z. Y. ^{[1
]}

机构：

[1] Yunnan Univ Finance & Econ, Kunming 650000, Yunnan, Peoples R China

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND INFORMATION TECHNOLOGY (SEIT2015) | 2016年

关键词：

XBRL; Frequent pattern; Parallel computing; Information disclosure;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Information disclosure system is the law that listed companies must obey in order to insure the benefits of investors and hold up to public scrutiny. Information like financial statement changes and operation condition of listed companies must be to the public according to the system. XBRL is an XML-based extensible language for exchanging business information. The language is widely used in financial information disclosure system and becomes the standard data format of the system. The specification, taxonomy and instance documents of XBRL are researched in this paper and the method of parallel data mining for the frequent pattern of massive XBRL data is proposed based on MapReduce and HDFS. The XBRL instances of the listed companies in China are processed by using this method and proved it to be effective.

引用

页码：138 / 142

页数：5

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