Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

基于大数据挖掘的赛珍珠文化元素提取与应用

目的 在大数据环境下,运用大数据技术提取赛珍珠文化元素,探究大数据挖掘理念在文创产品设计中应用的可行性.方法 首先,采集赛珍珠相关数据资料,借助网络爬虫工具采集网络媒体中赛珍珠相关的文本信息,同时人工搜集赛珍珠相关学术研究以及社会访谈资料,然后将数据保存为可编辑的文本形式.其次,运用中文分词工具对采集的文本信息进行处理,将语言字符串切分成词语,滤除中文停用词、低频词及干扰词,形成精炼的赛珍珠数据集合.之后,采用LDA主题模型算法对数据集合进行降维、聚类,形成初步的主题模型,然后经过人工筛选构建赛珍珠文化元素主题模型.最后,根据文化元素主题模型内容,选择赛珍珠文化元素进行赛珍珠文创产品设计实践.结论 依照大数据挖掘理念,通过对网络爬虫技术、中文分词工具以及LDA主题模型算法等大数据处理工具的综合应用,能够科学高效地从庞大的社会网络媒体中提炼赛珍珠文化元素,从而达到促进整个文创产品设计流程的效果.     

A Distributed Approach of Big Data Mining for Financial Fraud Detection in a Supply Chain

Supply Chain Finance (SCF) is important for improving the effectiveness of supply chain capital operations and reducing the overall management cost of a supply chain. In recent years, with the deep integration of supply chain and Internet, Big Data, Artificial Intelligence, Internet of Things, Blockchain, etc., the efficiency of supply chain financial services can be greatly promoted through building more customized risk pricing models and conducting more rigorous investment decision-making processes. However, with the rapid development of new technologies, the SCF data has been massively increased and new financial fraud behaviors or patterns are becoming more covertly scattered among normal ones. The lack of enough capability to handle the big data volumes and mitigate the financial frauds may lead to huge losses in supply chains. In this article, a distributed approach of big data mining is proposed for financial fraud detection in a supply chain, which implements the distributed deep learning model of Convolutional Neural Network (CNN) on big data infrastructure of Apache Spark and Hadoop to speed up the processing of the large dataset in parallel and reduce the processing time significantly. By training and testing on the continually updated SCF dataset, the approach can intelligently and automatically classify the massive data samples and discover the fraudulent financing behaviors, so as to enhance the financial fraud detection with high precision and recall rates, and reduce the losses of frauds in a supply chain.     

基于模型峰的气相色谱-质谱数据提纯算法

本文实现了一种基于"模型峰"的气相色谱-质谱(GC-MS)数据提纯算法,用于从GC-MS原始数据中提取各独立成分"干净"的谱图.算法中,首先利用质量色谱峰的高度从离子质量色谱图中预检出目标化合物的模型峰;然后对预检出的模型峰进行保留时间的校正,得到模型峰精确的保留时间;接着进行谱峰尖锐度的判定,得到目标化合物最终的模型峰.随后,基于所得到的目标化合物模型峰,比较每个离子质量色谱图与模型峰在保留时间和峰形上的相似度,决定该离子是否属于此模型峰.将所有具有相同保留时间和峰形的离子提取出来后,采用最小二乘法,得到各个独立的质谱峰的强度,组合成一张新的质谱图,从而获得目标化合物"干净"的谱图.最后,将10种有机酸混合进行实验,利用"模型峰"算法进行实验数据提纯,其结果验证了该算法的提纯效果与NIST 05自带退卷积软件AMDIS提纯效果一致.     

An efficient method for estimating from sparse data the parameters of the impact energy variation in the ductile-brittle transition region

An efficient method is proposed for estimating from sparse data the parameters of the systematic variation of the Charpy impact energy in the ductile-brittle transition region of low-carbon weld steels. The parameter estimates are practically unbiased and with a very good precision even in the case of very large scatter of the absorbed impact energy. Furthermore, the parameter estimates determining the shape of the transition curve are not affected by its location along the temperature axis. The method is robust regarding the temperature corresponding to a specified impact energy level. Thus, for different type of scatter of the impact toughness and different lengths of the scatter intervals, the estimates of the temperature corresponding to a specified impact energy vary in narrow limits. The transition temperature corresponding to a specified impact energy level is estimated with a very good precision, which is important for quantifying the deterioration of properties due to embrittlement.