Web大数据环境下的不一致跨源数据发现

Web中不同数据源之间的数据不一致是一个普遍存在的问题，严重影响了互联网的可信度和质量.目前数据不一致的研究主要集中在传统数据库应用中，对于种类多样、结构复杂、快速变化、数量庞大的跨源Web大数据的一致性研究还很少.针对跨源Web数据的多源异构特性和Web大数据的5V特征，将从站点结构、特征数据和知识规则3个方面建立统一数据抽取算法和Web对象数据模型；研究不同类型的Web数据不一致特征，建立不一致分类模型、一致性约束机制和不一致推理代数运算系统；从而在跨源Web数据一致性理论体系的基础上，实现通过约束规则检测、统计偏移分析的Web不一致数据自动发现方法，并结合这两种方法的特点，基于Hadoop MapReduce架构提出了基于层次概率判定的Web不一致数据的自动发现算法.该框架在Hadoop平台上对多个B2C电子商务大数据进行实验，并与传统架构和其他方法进行了比较，实验结果证明该方法具有良好的精确性和高效性.

Automatically Discovering of Inconsistency Among Cross-Source Data Based on Web Big Data

Data inconsistency is a pervasive phenomenon existing in Web, which has gravely affected the quality of Web information. The current research of data inconsistency mainly focused on traditional database application. It is lack of consistency research on diverse, complicated, rapidly-changing and abundant Web big data. On account of multi-source heterogeneous Web data and 5V features of big data, we present unified algorithm of data extraction and Web object data model based on three aspects: website structure, characteristic data and knowledge rules. We study and sort the features of data inconsistency, and establish inconsistency classifier model, inconsistency constraint mechanism and inconsistency inference algebra computing system. Then based on cross-source Web data consistency theory system, we've researched Web inconsistency data automatically discovery method via constraint rules detection and statistical deviation analysis. Combining the characters of the two methods, we propose an automatically discovery algorithm of Web inconsistency data in view of hierarchy probabilistic judgment based on Hadoop MapReduce architecture. The framework is applied to multiple B2C electronic commerce big data on Hadoop platform and compared with traditional architecture and other methods. The results of our experiment proves the accuracy and efficiency of the method.