摘要:互联网中，以网页、社交媒体和知识库等为载体呈现的大量非结构化数据可表示为在线大图.在线大图数据的获取包括数据收集和更新，是大数据分析与知识工程的重要基础，但面临着数据量大、分布广、异构和变化快速等挑战.基于采样技术，提出并行、自适应的在线大图数据收集和更新方法.首先，将分支限界方法与半蒙特卡罗采样技术相结合，提出能够自适应地收集在线大图数据的HD-QMC算法；然后，为了使收集的数据能反映实际中在线大图的动态变化，进一步基于信息熵及泊松过程，提出高效更新在线大图数据的EPP算法.从理论上分析了该算法的有效性，并将获取的各类在线大图数据统一表示为RDF三元组的形式，为在线大图数据分析及相关研究提供方便易用的数据基础.基于Spark实现了在线大图数据的收集和更新算法，人工生成数据和真实数据上的实验结果展示了该方法的有效性和高效性.



Abstract:The large volume of unstructured data obtained from Web pages, social media and knowledge bases on the Internet could be represented as an online big graph (OBG). Confronted with many challenges, such as its large-scale, widespread, heterogeneous, and fast-changing properties, OBG data acquisition includes data collection and updating, which is the basis of massive data analysis and knowledge engineering. In this study, the method for adaptive and parallel data collection and updating is proposed based on sampling techniques. First, the HD-QMC algorithm is given for adaptive data collection of OBG data by combining the branch-and-bound method and quasi-Monte Carlo sampling technique. Next, the EPP algorithm is given for efficient data updating based on entropy and Poisson process to make the collected data reflect the dynamic change of OBGs in real-world environments. Further, the effectiveness of the proposed algorithms is analyzed theoretically, and various kinds of collected OBG data are represented by triples universally to provide an easy-to-use data foundation for OBG analysis and relevant studies. Finally, the proposed algorithms for data collection and updating are implemented with Spark, and experimental results on simulated and real-world datasets show the effectiveness and efficiency of the proposed method.



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