Smart Data for Digital Humanities

The emergence of ＂Big Data＂ has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being--＂Smart Data.＂ This paper shares the author＇s understanding of what, why, how, who, where, and which data in relation to Smart Data and digital humanities. It concludes that, challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.

Data Virtualization with SAP HANA Smart Data Access

Digital transformation has been corner stone of business innovation in the last decade, and these innovations have dramatically changed the definition and boundaries of enterprise business applications. Introduction of new products/ services, version management of existing products/ services, management of customer/partner connections, management of multi-channel service delivery (web, social media, web etc.), merger/acquisitions of new businesses and adoption of new innovations/technologies will drive data growth in business applications. These datasets exist in different sharing nothing business applications at different locations and in various forms. So, to make sense of this information and derive insight, it is essential to break the data silos, streamline data retrieval and simplify information access across the entire organization. The information access framework must support just-in-time processing capabilities to bring data from multiple sources, be fast and powerful enough to transform and process huge amounts of data quickly, and be agile enough to accommodate new data sources per user needs. This paper discusses the SAP HANA Smart Data Access data-virtualization technology to enable unified access to heterogenous data across the organization and analysis of huge volume of data in real-time using SAP HANA in-memory platform.

A Brief Introduction to Infrastructure Planning for Next-Generation Smart Computing Data Centers

Globally,digital technology and the digital economy have propelled technological revolution and industrial change,and it has become one of the main grounds of international industrial competition.It was estimated that the scale of China’s digital economy would reach 50 trillion yuan in 2022,accounting for more than 40%of GDP,presenting great market potential and room for the growth of the digital economy.With the rapid development of the digital economy,the state attaches great importance to the construction of digital infrastructure and has introduced a series of policies to promote the systematic development and large-scale deployment of digital infrastructure.In 2022 the Chinese government planned to build 8 arithmetic hubs and 10 national data center clusters nationwide.To proactively address the future demand for AI across various scenarios,there is a need for a well-structured computing power infrastructure.The data center,serving as the pivotal hub for computing power,has evolved from the conventional cloud center to a more intelligent computing center,allowing for a diversified convergence of computing power supply.Besides,the data center accommodates a diverse array of arithmetic business forms from customers,reflecting the multi-industry developmental trend.The arithmetic service platform is consistently broadening its scope,with ongoing optimization and innovation in the design scheme of machine room processes.The widespread application of submerged phase-change liquid cooling technology and cold plate cooling technology introduces a series of new challenges to the construction of digital infrastructure.This paper delves into the design objectives,industry considerations,layout,and other dimensions of a smart computing center and proposes a new-generation data center solution that is“flexible,resilient,green,and low-carbon.”

基于SMART数据模式的HDD硬盘状态预测方法

硬盘广泛应用于各类信息系统中,其工作状态预测对信息系统的正常运行管理有着重要意义。现有基于SMART(Self Monitoring Analysis and Reporting Technology)属性的机器学习预测算法为保证其通用性,普遍选取部分典型属性作为特征,带来一定的信息丢失。在分析SMART数据特点的基础上,提出数据模式分类后再进行机器学习预测的SMART数据处理方法。实际测试结果表明,经分类处理后,采用简单的机器学习算法即可获得与强分类器接近的性能,同时,该方法可有效简化SMART数据机器学习时的特征选择过程,有效降低算法的资源消耗。

Exploring the Evolution of Passenger Flow and Travel Time Reliability with the Expanding Process of Metro System Using Smartcard Data

Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.

Analysis of passenger boarding time difference between adults and seniors based on smart card data

As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.

Big Metadata,Smart Metadata,and Metadata Capital:Toward Greater Synergy Between Data Science and Metadata

Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science. Design/methodology/approach： This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings： The ＂utilitarian nature＂ and ＂historical and traditional views＂ of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part ofa metadata linguafranca to help frame research in the data science research space. Research limitations： There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications： The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem. Originality/value： Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio,energy efficiency,end-to-end delays etc.However,traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics.In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case,an efficient schedulingmechanism ismandatory which is the main concern of this paper.In this paper,we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications.Transmission information about the priority packets between the source nodes(i.e.,people with emergency cases)and the destination nodes(i.e.,hospitals)is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties.In our proposed mechanism,Software Defined Networking(SDN)with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments.In this paper,we compare between our proposed Priority Packets Deadline First scheduling scheme(PPDF)with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time,packets loss rates,priority packets end-to-end delay,and efficient energy consumption.

Real-Time Geospatial Data Collection and Visualization with Smartphone

The development of the Global Navigation System and wireless networking technologies have changed the way we live, communicate, share information and even the collection of geospatial data in the field. Along with wireless networking technologies, the improvement in computational power of handheld devices such as smartphones, tablet PCs, ultra-mobile personal computers (UMPCs) and netbook computers allow field users to connect, store and stream large amounts of geospatial data from the web-server. Nowadays, geospatial data collection is more flexible and timely manner. In this paper we discuss field data collection using a smartphone and web-based GIS system, which collects, integrates, visualizes and analyzes the collected data in real-time. We built a web-GIS system for creating a user account, acquiring coordinates from GPS embedded devices or wireless access points, and providing a user-friendly survey form. The collected data can be visualized and analyzed by performing thematic mapping, labeling, symbolizing, querying and generating a summary report. We tested this system on a university campus management system, in which we collected information on illegal disposal sites and parking events within the university campus.

An Anti-Eavesdropping Method in Data Collection of Smart Meter

At present, DL/T 645-2007 communication protocol is used to collect data for smart meters. However, in the beginning, this protocol is not designed to be a secure protocol and only the function and reliability were taken into account. Plaintext is used in the protocol for data transmission, as a result, attackers can easily sniff the information and cause information leakage. In this paper, man-in-the-middle attack was used to verify that the smart meter data acquisition process was vulnerable when facing third-party attacks, and this can result in data eavesdropping. In order to resist such risks and prevent information being eavesdropped, a real ammeter communication experimental environment was built, it realized two-way identity authentication between data acquisition center and ammeter data center. At the same time, RSA (Rivest-Shamir-Adleman) was used to encrypt the meter data, which encrypted the collection, storage process of meter data and ensured the confidentiality and integrity of the meter data transmission. Compared with other methods, this method had obvious advantages. The analysis showed that this method can effectively prevent the data of smart meters from being eavesdropped.

Big Data Analysis in Smart Manufacturing: A Review

The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

科技情报智慧数据:方法、体系与应用

[目的/意义]分析智慧数据建设现状,认识人工智能时代科技情报智慧数据建设的方法与框架。[方法/过程]界定科技情报智慧数据的概念内涵,提出“明确定位与价值、数据架构体系、建设知识语料、数据质量控制、嵌入业务流程的协同建设模式、数据权益约束及安全分级”7个方面的建设原则,设计“技术与功能逻辑、数据分类组织、数据建设流程、数据协同加工工具、运营服务策略”体系框架,拓展智慧数据的示范应用。[结果/结论]通过研发具有“收、存、治、管、用”一体化的科情数据平台,建立了覆盖“情报动态、科研成果、创新活动、创新主体及多类功能型知识实体”等类型的科技情报智慧数据中心,并在“科技人才自动发现、重大科技问题的自动生成以及语义查重查新”等场景中开展应用。

科技情报智慧数据服务体系建设研究

[目的/意义]在数据驱动环境下,科技情报工作愈加重视基于数据的循证分析,建设智慧数据服务体系是科技情报工作向数据驱动赋能转型的重要动力。[方法/过程]界定了智慧数据服务的概念内涵与外延,从数据视角与服务视角两个维度出发阐述了智慧数据服务的特点,论述了科技情报智慧数据服务体系的建设目标;以此为基础,依据从数据到情报的服务逻辑与层级,提出了科技情报智慧数据服务体系的内容架构与建设模式。[结果/结论]从数据供给站、数据超市、数据应用产品、数据服务中台4个方面构建科技情报智慧数据服务体系,前两者侧重数据供给型服务,旨在满足用户的数据型需求,后两者侧重数据感知型服务,旨在满足用户的情报型需求;提出需求与建设并行、治理与协同并行、技术与内容并行、质控与运营并行的“四个并行”建设模式;并以中国科学院文献情报中心的智慧数据服务体系建设为案例,阐述了智慧数据服务体系的应用落地。

智慧供热系统数据运行核查方法研究

高质量数据是智慧供热系统数据分析的前提和分析结论可靠性的保障,计量器具的良好工作状态是获得高质量数据的基础。本文讨论了计量器具的检定、校准及运行核查的区别,研究了智慧供热系统计量器具在线运行核查方法。研究表明:流量计根据其实际安装工作条件,采用安装条件修正系数可解决直管段长度不够带来的测量误差问题;采用基准表法对使用的流量计进行技术性能核查,可正确判别单台流量计的工作状态;示踪法是解决大口径贸易结算表数据质量问题的有效方法。本研究可为及时发现工作异常的计量器具、确保测量数据的准确性及可靠性提供理论指导及技术支撑。

基于侧链和信任管理模型的数据共享方案

为解决在不信任的区块链环境中由于数据转售而造成的用户不愿共享数据等问题,提出一种基于侧链和信任管理模型的数据共享方案。使用信任管理模型量化节点信任值,将节点信任值集成到数据共享智能合约中,为用户提供可信的共享环境和动态的访问控制接口。对联盟链进行侧链扩容,使用信任管理模型优化共识算法。规定主链使用具有拜占庭容错能力的T-PBFT算法,侧链使用仅适用于高信任场景的T-Raft算法。用户根据预先设置的阈值动态决定在主链或侧链共享数据。实验就方案的安全性和性能进行分析,表明了该方案的可行性和有效性。

基于区块链的联邦蒸馏数据共享模型研究

零散、孤立的海量数据形成“数据孤岛”使得数据无法交互和连接,如何在保护原始数据隐私的前提下安全有效地共享数据中的知识信息已成为热点研究问题。基于以上内容,提出了一种基于区块链的联邦蒸馏数据共享模型(BFDS)。区别于中心化架构,采用区块链联合多参与方组建教师网络,实现分布式协同工作;通过交换蒸馏输出的方式,传递数据中的知识信息,联合训练轻量化模型;提出了一种多权重节点可信评估算法,调用智能合约分配权重并生成可溯源全局软标签,降低因参与方质量差异而产生的负向影响。实验结果表明,BFDS模型能联合多参与方安全可信共享数据知识,协同蒸馏训练模型,降低了模型的部署成本;所提出的多权重节点评估算法能有效减小低质量节点的负向影响,提高了全局软标签的质量与安全性。

Data Compression for Next Generation Phasor Data Concentrators (PDCs) in a Smart Grid

The storage space and cost for Smart Grid datasets has been growing exponentially due to its high data-rate of various sensor readings from Automated Metering Infrastructure (AMI), and Phasor Measurement Units (PMUs). The paper focuses on Phasor Data Concentrators (PDCs) that aggregate data from PMUs. PMUs measure real-time voltage, current and frequency parameters across the electrical grid. A typical PDC can process data from anywhere ten to forty PMUs. The paper exploits the need for appropriate security and data compression challenges simultaneously. As a result, an optimal compression method ER1c is investigated for efficient storage of IREG and C37.118 timestamped PDC data sets. We expect that our approach can greatly reduce the storage cost requirements of commercial available PDCs (SEL 3373, GE Multilin P30) by 80%. For example, 2 years of PDC data storage space can be easily replaced with only 10 days of storage space. In addition, our approach in combination with AES 256 encryption can protect PDC data to larger degree as per National Institute of Standards and Technology (NIST) standards.

数智时代面向智库的图书馆智慧数据服务体系构建与推进策略

文章分析国外高校智库智慧数据服务内容,以图书馆智慧数据服务生命周期和智库研究DIIS理论为基础构建面向智库的图书馆智慧数据服务体系。该体系以数智赋能为动力,包含感知获取、融合处理、综合分析、交互服务4个层次,各层次相互依赖,共同作用于面向智库的智慧数据决策支持,据此提出面向智库的图书馆智慧数据服务策略,即促进情境感知驱动,增强智慧数据服务动力;整合资源要素,构建智慧数据集成网络;强化数智技术赋能,实现智慧数据全流程服务;搭建数据共享平台,提升智慧数据服务效能。

Power Quality Data Compression Based on Iterative PCA Algorithm in Smart Distribution Systems

To reduce the stress of data transmission and storage for power quality (PQ) in smart distribution systems and help PQ analysis, a multichannel data compression based on iterative PCA (principal component analysis) algorithm is introduced. The proposed method uses PCA to reduce the redundancy of data to achieve the purpose of compressing data. In order to improve the calculating speed, an iterative method is proposed to compute the principal components of the covariance matrix. The correctness and feasibility of the proposed method are verified by field PQ data tests. Compared with discrete wavelet transform (DWT) method, the proposed method has good performance on compression ratio and reconstruction accuracy.

基于赛教融合的智能交通课程实践教学研究

结合全国大学生智能汽车竞赛百度智慧交通创意组竞赛项目,设计智能交通课程实践教学内容,从竞赛内容凝练与课程知识点结合的教学案例和实验项目,实现赛教资源融合和赛教一体化实践平台共享。设计的实验教学项目融合了图像处理、数据分析、人工智能、智能车硬件平台等内容,包括数据采集、数据分析、数据集构建,深度学习模型建立与智能车平台部署等实践环节。学生从编写简单程序开始,不断提高,到构建复杂的深度学习模型实现智能车自动巡航控制实验。将竞赛任务具有挑战性和对抗性的特点融入课程教学,吸引学生兴趣,增强学生求知欲,提升学生创新能力和工程实践能力。