过程数据采集系统PDA在轧钢过程的应用

过程数据采集系统PDA不仅能对数据进行采集和分析,在解决实际问题中,数据采集、分析和处理的快速性和准确性问题就突出的表现出来。在轧钢的过程中,需要对它的各种运行数据进行采集、归档,以便实时在线分析、排查各种运行故障,保障生产线运行在最佳状态。本文结合莱钢冷轧厂轧钢过程的过程数据采集系统,介绍了IBA公司的高速数据采集软件在轧钢生产上的应用。此系统解决了重要数据的高速采集以及存储等问题,为系统优化和故障分析提供了依据。     

分布式产品数据采集动态任务调度模型研究

网络数据采集是大数据时代进行数据挖掘和分析的基础性工作。本文尝试着以任务在不同节点上采集过程中产生的信息作为调度指标制定动态任务调度策略,分别从任务调度策略、任务修改策略和任务回收策略3个角度构建任务调度模型,最后通过实验分析其可行性。实验结果表明,动态任务调度模型能够提高数据采集的效率。     

虚拟仪器技术及其在数据采集中的应用

本文介绍了虚拟仪器技术,并在LabVIEW虚拟仪器软件开发环境下,完成了以NI公司M系列数据采集（DAQ）卡PCI-6221为核心的数据采集系统的设计,实验证实了LabVIEW在数据采集应用中的优越性。     

基于PIC18F455O的环境监测数据采集系统设计

介绍了基于PIC18F4550单片机和USB-HID类的数据采集系统,并列举了该系统在环保分析仪器中的应用实例.详细介绍了相关的电路设计、单片机固件程序和上位机程序的开发流程.该方案具有实用性强,成本低廉,开发周期短等特点.     

一种电子政务数据采集交换系统的研究与实现

介绍了一种电子政务数据采集交换系统的研究与实现,该系统从整合电子政务信息资源建设的角度出发,应用JAVA、XML技术在省级电子政务广域网络上实现不同应用系统数据的访问、采集和交换,以此来实现电子政务信息资源的整合、共享和利用。     

数据采集及信息集成系统设计与应用

本文针对数据采集及信息集成系统采集信号的分类进行分析,结合数据采集及信息集成系统设计功能,包括采集数据、处理数据、数据咨询、数据传输等,通过研究软件模块化处理、数据容错处理、数据库更新、界面友好等系统应用效果,目的在于提高系统设计水平,促进行业经济的稳定发展。     

机载数据采集系统抗干扰技术研究

数据采集系统在应用过程中如果屏蔽接法不正确,布线和接地不合理造成的干扰,可直接影响到飞机试飞数据的有效性。本文对屏蔽和接地抗干扰技术进行了分析,并提出了合理布线、屏蔽和接地的实现方法,以提高数据采集系统抗干扰能力,使测试采集系统更准确、稳定可靠,保证科研试飞顺利进行。     

多通道数据采集装置的研制

文章介绍了一种以80C51、AD1674作为核心器件的多通道、高精度、快速数据采集系统的结构性能及工作原理该系统通过模拟开关AD7501对8路变送器信号进行采集．采集数据连续可靠．能长时间不间断工作，并可通过串行接口RS-485与IBM-Pc上位机通信，进行数据转存及数据管理.     

基于SPCE061A单片机水温数据采集系统

随着时代的进步、科技的发展,智能化水温数据采集和控制已经成为了数据采集和控制的主流。水温的数据采集和控制在日常生活和工作中应用的非常广泛,在很多场所的水温都需要数据的采集和监控以防止发生意外。系统以PT100温度传感器采集温度信号器件、SPCE061A单片机为核心控制器件,数码管作为数字信号输出器件,对采集到的水温进行运算,并实现对水温数据的精准采集和控制。基于SPCE061A单片机水温数据采集系统,它可以更精准的采集水温的数据和更高效的的控制水温。控制系统可以通过电脑设置一个恒定的温度,将水环境数据与所设定数据进行比较,当前温度高于恒定温度时,会自动警报。     

GigaBASE在数据采集系统软件中的应用

采用模块化设计方法开发数据采集系统软件时,不同数据记录方式的优缺点,主要介绍对象关系数据库GigaBASE的特性以及如何使用它实现分布式雷电定位系统软件的数据存储模块。测试结果表明:使用GigaBASE管理采集数据安全可靠、查询方便快捷,广泛适用于高、低速数据采集系统软件。     

Open data: Quality over quantity

Open data aims to unlock the innovation potential of businesses, governments, and entrepreneurs, yet it also harbours significant challenges for its effective use. While numerous innovation successes exist that are based on the open data paradigm, there is uncertainty over the data quality of such datasets. This data quality uncertainty is a threat to the value that can be generated from such data. Data quality has been studied extensively over many decades and many approaches to data quality management have been proposed. However, these approaches are typically based on datasets internal to organizations, with known metadata, and domain knowledge of the data semantics. Open data, on the other hand, are often unfamiliar to the user and may lack metadata. The aim of this research note is to outline the challenges in dealing with data quality of open datasets, and to set an agenda for future research to address this risk to deriving value from open data investments.     

Performance prediction of data fusion for information retrieval

The data fusion technique has been investigated by many researchers and has been used in implementing several information retrieval systems. However, the results from data fusion vary in different situations. To find out under which condition data fusion may lead to performance improvement is an important issue. In this paper, we present an analysis of the behaviour of several well-known methods such as CombSum and CombMNZ for fusion of multiple information retrieval results. Based on this analysis, we predict the performance of the data fusion methods. Experiments are conducted with three groups of results submitted to TREC 6, TREC 2001, and TREC 2004. The experiments show that the prediction of the performance of data fusion is quite accurate, and it can be used in situations very different from the training examples. Compared with previous work, our result is more accurate and in a better position for applications since various number of component systems can be supported while only two was used previously.     

The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services

Over the past few years, data mining has moved from corporations to other organizations. This paper looks at the integration of data mining in digital library services. First, bibliomining, or the combination of bibliometrics and data mining techniques to understand library services, is defined and the concept explored. Second, the conceptual frameworks for bibliomining from the viewpoint of the library decision-maker and the library researcher are presented and compared. Finally, a research agenda to resolve many of the common bibliomining issues and to move the field forward in a mindful manner is developed. The result is not only a roadmap for understanding the integration of data mining in digital library services, but also a template for other cross-discipline data mining researchers to follow for systematic exploration in their own subject domains.     

一种基于CCSDS IDC标准的可伸缩性视频编码算法

空间通信具有资源受限和差错率高等特点。国际空间数据系统顾问委员会（CCSDS）所制定的图像数据压缩标准（IDC)）一种应用于空间通信的图像编码标准。但该标准未能利用序列的时域相关性,仅适用于图像编码,不适应于空间视频通信。因此,结合运动补偿时域滤波,提出了一种基于CCSDS IDC的可伸缩性视频编码算法以及一种新颖的动态帧分组算法。实验结果表明：该算法具有良好的编码性能,并能适应于空间通信网带宽的动态变化。     

Laws and Regulations tell how to classify your data: A case study on higher education

The era of big data has promoted the vigorous development of many industries, boosting the full potential of holistic data-driven analysis, yet it has also been accompanied by uninterrupted data breaches. In recent years, especially in China, data security laws and regulations have been promulgated continuously, and many of them have made clear requirements for data classification. As the support of data security initiatives, data classification has received the bulk of attention and has been hailed by all walks of life. There is a lot of valuable information contained in the issued regulations, which has already been well exploited in the research of privacy policy compliance verification, whereas few scholars have drawn on such information to guide data classification for security and compliance. As a step towards this direction, in this paper, we define two information types: one is “regulated data” mentioned in external laws and regulations, another is “non-regulated data”, indicating internal business data produced in a certain organization, and develop a novel generalization-enhanced decision tree classification algorithm called Gen-DT to classify data. In this way, data covered by the relevant data security regulatory mandates can be quickly identified and handled in full compliance as well. Furthermore, we evaluate the proposed compliance-driven data classification scheme using datasets collected from two famous universities in China and validate that our approach can achieve better performance than existing popular machine learning techniques.     

Developing scalable management information system with big financial data using data mart and mining architecture

The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix.     

Support the Manchester Manifesto: a case study of the free sharing of human genome data

Professor Huanming Yang is co-founder and president of BGI Shenzhen (formally the Beijing Institute of Genomics). BGI made China’s contribution to the human genome project. From 1% of the human genome project, it is now a key player in many of the world’s megasequencing projects. Professor Yang also has a strong interest in bioethics and society. He is a former member of UNESCO’s international bioethics committee and has recently been appointed to President Obama’s international research panel of the presidential commission for the study of bioethical issues.     

Online portfolio management via deep reinforcement learning with high-frequency data

Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.     

A review and future direction of agile,business intelligence,analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.     

The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage

The number of firms that intend to invest in big data analytics has declined and many firms that invested in the use of these tools could not successfully deploy their project to production. In this study, we leverage the valence theory perspective to investigate the role of positive and negative valence factors on the impact of bigness of data on big data analytics usage within firms. The research model is validated empirically from 140 IT managers and data analysts using survey data. The results confirm the impact of bigness of data on both negative valence (i.e., data security concern and task complexity), and positive valence (i.e., data accessibility and data diagnosticity) factors. In addition, findings show that data security concern is not a critical factor in using big data analytics. The results also show that, interestingly, at different levels of data security concern, task complexity, data accessibility, and data diagnosticity, the impact of bigness of data on big data analytics use will be varied. For practitioners, the findings provide important guidelines to increase the extent of using big data analytics by considering both positive and negative valence factors.