Analysis and Enlightenment of Big Data Platform for Adverse Drug Reaction Supervision in China and the United States

Objective To introduce the relevant big data platforms of FDA regulatory sciences and to provide reference for the construction of big data platform for China’s regulatory science under the“14th five-year plan”to deepen the reform of medical and health system.Methods A comparative analysis was made on China’s big data for regulatory science after studying the development process,operation mode,practical significance and characteristics of the big data platform for FDA regulatory science,which would help China to establish a perfect big database.Results and Conclusion The construction of big data platform for China’s regulatory science is not comprehensive compared with that in the United States.It is necessary to build data platforms in line with China’s national conditions through efforts in law,talents,standards,and other aspects.

The data platform of national special environment and disaster field observation stations based on grid environment

The Data Platform of Resource and Environment—whose data mainly come from field observation stations,spatial observations,and internet service institutions—is the base of data analysis and model simulation in geoscience research in China.Among this integrated data platform,the tasks of the data platform of field observation stations are principally data collection,management,assimilation,and share service.Taking into consideration the distributing characteristics of the data sources and the service objects,the authors formulated the framework of the field observation stations' data platform based on the grid technology and designed its operating processes.The authors have further defined and analyzed the key functions and implementing techniques for each module.In a Linux operating system,validation tests for the data platform's function on data replication,data synchronization,and unified data service have been conducted under an environment that of the simulating field stations.

Accuracy improvement of the traditional rolling force and torque model powered by an industrial data platform

The calculation results of the rolling force and torque model based on Orowan's differential equation numerical solution method do not fit with the industrial measurements very well.In particular,a quite large deviation on the torque model was found.On the basis of analyzing the shortcomings of the existing method,an improved rolling force and torque model algorithm aided by the Process Integrated Data Application System platform is proposed.Accordingly,the calculation accuracy of the rolling torque model is improved.The improved models are verified by 1711136 records of a data platform.The improved models are also based on Orowan's differential equation.Two coefficients,namely,friction factor and forward slip,are recognized as the crucial factors to be determined from industrial measurements to improve the accuracy.Therefore,the proposed method is a hybrid method that can be used to deeply understand the rolling process and improve the model's accuracy by combining traditional plastic mechanics and data-driving global optimization algorithms.This paper proposes a new approach to studying theoretical rolling deformation models powered by the industrial data platform.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

基于DataOps的企业管理业务全链路的数据中台解决方案

针对企业管理数字化存在的问题,融入DataOps(数据开发即治理)理念,建立一体化敏捷开发平台,提供异构数据源快速的同步能力,设计标准高效、轻量化、可视化、易运维的数据采集、开发工具,提供标准统一的数据共享服务,通过实际应用表明此方案可缩短开发周期,显著提升数据开发效率,有效提升数据治理、分析、服务能力,充分体现数据资源的价值,获得显著应用效果。

Mobile Internet Big Data Platform in China Unicom

China Unicorn, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate （CAGR） of 135%. Currently China Unicorn monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System （HDFS） as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

State of data platforms for connected vehicles and infrastructures

The continuing expansion of connected and electro-mobility products and services has led to their ability to rapidly generate very large amounts of data,leading to a demand for effective data management solutions.This is further catalysed through the need for society to make informed policies and decisions that can properly support their emerging growth.While data systems and platforms exist,they are often proprietary,being only compatible to the products that they are designed for.Given the products and services generate energy and spatial-temporal data that can often correlate,a lack of interoperability between these systems would impede decision making,as data from each system must be considered independently.By studying currently available data platforms and frameworks,this paper weighs the problems that these products address,and identifies necessary gaps for a more cohesive platform to exist.This is performed through a top-down approach,whereby broader vehicle-toeverything approaches are first studied,before moving to the components that could comprise a data platform to integrate and ingest these various data feeds.Finally,potential design considerations for a data platform is presented,along with examples of application bene.

Interactive Generalization on Large-Scale Topographical Map Supported by a Database Platform

This paper makes astudy on the interactive digital gener-alization, where map generalizationcan be divided into intellective reason-ing procedure and operational proce-dure, which are done by human andcomputer, respectively. And an inter-active map generalization environmentfor large scale topographic map is thendesigned and realized. This researchfocuses on: ① the significance of re-searching an interactive map generali-zation environment, ② the features oflarge scale topographic map and inter-active map generalization, ③ the con-struction of map generalization-orien-ted database platform.

Preliminary build and application of a data analysis platform for coiled tubing steel strips

To solve the problems in the quality control and improvement of coiled tubing steel strips production, such as scattered and inefficient production data, difficult performance fluctuation factor analysis, complex multivariate statistical analysis, and low accuracy and difficulty in mechanical property prediction, an industrial data analysis platform for coiled tubing steel strips production has been preliminarily developed.As the premise and foundation of analysis, industrial data collection, storage, and utilization are realized by using multiple big data technologies.With Django as the agile development framework, data visualization and comprehensive analyses are achieved.The platform has functions including overview survey, stability analysis, comprehensive analysis(such as exploratory data analysis, correlation analysis, and multivariate statistics),precise steel strength prediction, and skin-passing process recommendation.The platform is helpful for production overviewing and prompt responding, laying a foundation for an in-depth understanding of product characteristics and improving product performance stability.

The Practice-oriented Platform of“Data Structure and Algorithm”Curriculum

"Data Structure and Algorithm",which is an important major subject in computer science,has a lot of problems in teaching activity.This paper introduces and analyzes the situation and problems in this course study.A "programming factory" method is then brought out which is indeed a practice-oriented platform of the teachingstudy process.Good results are obtained by this creative method.

从技术赋能到整体重塑:公共体育服务数字化治理的模式重构、组织变革及推进机制

数字时代我国公共体育服务从“技术赋能”到“整体重塑”的数字化进程被视为一个重塑多元主体治理关系及其能力的演变历程,对于提高公共体育服务供给效率具有重要意义。运用文献资料法、逻辑分析法等厘清了公共体育服务数字化治理的基本内涵及现存困境,重新审视了其模式重构的核心维度,深入阐释了其组织变革的动因特征,重点讨论了其推进机制的实践要素。研究认为:理念重构、价值重构、方式重构等形成模式重构的核心维度;管理思维转换、运行范式转换、决策方式转换等构成组织变革的动因特征;管理机制、考核机制、协调机制等为推进机制的实践要素。

The IRIS Development Platform and Proposed Object-Oriented Data Base

Various code development platforms, such as the ATHENA Framework [1] of the ATLAS [2] experiment encounter lengthy compilation/linking times. To augment this situation, the IRIS Development Platform was built as a software development framework acting as compiler, cross-project linker and data fetcher, which allow hot-swaps in order to compare various versions of software under test. The flexibility fostered by IRIS allowed modular exchange of software libraries among developers, making it a powerful development tool. The IRIS platform used input data ROOT-ntuples [3];however a new data model is sought, in line with the facilities offered by IRIS. The schematic of a possible new data structuring—as a user implemented object oriented data base, is presented.

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.

基础教育资源配置大数据分析平台的整体架构与运行保障

教育大数据是反映基础教育资源配置现状、需求和问题的数据集合,也是重塑基础教育资源配置结构、构建良好教育生态的有效工具。教育大数据能够为资源精准配置提供客观依据、助力理性决策、拓展实践路径、提供监测反馈,可从数据资源层、处理层、分析层和可视化层,整体架构基础教育资源配置大数据分析平台,实现多学段基础教育数据精准采集、多模态基础教育数据分类储存、多因素基础教育数据指标计量、多维度基础教育数据结果呈现。基础教育资源配置大数据分析平台顺利运行的保障措施主要包括:政策跟进,赋予数据驱动合法地位;强化治理,保障平台教育数据质量;注重成效,发挥数据分析平台价值;多元联动,助力数据分析平台共建。通过这些措施,努力实现数据驱动基础教育资源优化配置,促进决策科学化、供给精准化、评估动态化。

国医大师熊继柏治疗失眠的方药规律分析

目的对国医大师熊继柏教授治疗失眠的病案进行数据挖掘,分析熊继柏教授诊治失眠的用方、用药经验。方法基于中医传承辅助平台软件(V3.5),归纳和分析熊继柏教授在湖南中医药大学第一附属医院治疗失眠患者的门诊病历数据,利用该平台软件的关联规则、复杂系统熵聚类等统计方法进行数据挖掘和分析,总结药物的性味、归经、应用频次、药对组合及合方,对“证-症-方”“证-症-药”“证-方-药”进行关联,结合熊继柏教授对失眠病因病机、治则治法的认识,明确核心用药,探索其对失眠的诊治规律及遣方用药心得。结果通过总结分析,共纳入病历276份,处方中用药频次较多的有酸枣仁、龙齿、茯神、甘草,重在补虚安神;黄连、陈皮、竹茹、半夏、珍珠母、远志,重在清热化痰安神。用药性味偏甘、苦、辛,归经多为心、脾经。并归纳出虚证治疗常用合方为酸枣仁汤、枕中丹和甘麦大枣汤;因痰所致失眠治疗的主方为温胆汤及温胆汤类方(枣仁温胆汤、黄连温胆汤及十味温胆汤)。结论国医大师熊继柏治疗失眠以“补脾化痰清热,养血补肝宁心”为治疗原则,以温胆汤为基础进行加减化裁。

政府开放数据平台FAIR原则评估指标体系及实证研究

[目的/意义]评估政府开放数据平台FAIR原则的实施效果有助于平台规范标识符、统一元数据标准等,增强政府数据的发现、共享和重用。[方法/过程]采用网络调研法,借鉴国际代表性评估框架,结合调研构建面向政府开放数据平台的FAIR原则评估指标体系,并选取国内外8个样本平台作为案例,从可发现、可访问等4个维度展开评估分析,比较FAIR原则实施状况和差异。[结果/结论]从永久性标识符方案、元数据标准规范、数据使用许可等方面提出相应改进策略,完善国内政府开放数据平台数据管理规范,为后续相关平台的建设与利用提供理论指导和实践借鉴。