网络信息披露制度下XBRL金融数据的频繁模式并行挖掘

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

信息披露制度是上市公司为保障投资者利益、接受社会公众的监督而依照法律规定必须将其自身的财务变化、经营状况等信息向社会及监管部门公开或公告,以便投资者充分了解情况的制度.XBRL作为一种基于XML的可扩展性商业报告语言,目前已广泛应用于财务信息披露制度中,并逐渐成为了信息披露制度的标准数据格式.对XBRL的规范、分类、实例文档进行研究,基于MapReduce和HDFS提出可用于海量XBRL数据的频繁模式并行挖掘方法,基于我国上市公司的XBRL实例数据进行了实验,取得了良好的效果.

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 in-stance 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.