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 New ETL Approach Based on Data Virtualization

ETL （Extract-Transform-Load） usually includes three phases： extraction, transformation, and loading. In building data warehouse, it plays the role of data injection and is the most time-consuming activity. Thus it is necessary to improve the performance of ETL. In this paper, a new ETL approach, TEL （Transform-Extract-Load） is proposed. The TEL approach applies virtual tables to realize the transformation stage before extraction stage and loading stage, without data staging area or staging database which stores raw data extracted from each of the disparate source data systems. The TEL approach reduces the data transmission load, and improves the performance of query from access layers. Experimental results based on our proposed benchmarks show that the TEL approach is feasible and practical.

Cost-Aware Multi-Domain Virtual Data Center Embedding

Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.

基于Data Fabric的多模数据管理方法

随着政府和企业在信息化向数字化演进历程中信息化程度的加深,各类应用系统生成的数据日益多模化、多源化、海量化,这对数据管理造成了新的挑战.为了解决这些挑战,数据管理领域涌现出了许多新的技术和理念,其中Data Fabric (数据编织)便是一种新兴的数据管理技术和方法,它将分布式数据存储、处理和应用整合为一个整体,并提供了一套可视化的接口进行管理.本文首先分析了Data Fabric的技术架构、技术特点、技术价值和对多模数据进行管理与应用的完整流程.其次,提出了基于时序指标的多模多源数据的异常监测方法、基于日志数据的多模多源数据的异常监测方法,它们通过Data Fabric技术的使用,处理速度分别提高了33.3%和42.2%, F1-score分别提高12.2个和14.8个百分点,进一步说明了Data Fabric技术和本文新提出方法的高效性和应用价值.

Study on Building and Modeling of Virtual Data of Xiaosha River Artificial Wetland

From the viewpoint of systems science, this article takes Xiaosha River artificial wetland under planning and construction as object of study based on the systems theory and takes the accomplished and running project of Xinxuehe artificial wetland as reference. The virtual data of quantity and quality of inflow and the quality of outflow of Xiaosha River artificial wetland are built up according to the running experience, forecasting model and theoretical method of the reference project as well as the comparison analysis of the similarity and difference of the two example projects. The virtual data are used to study the building of forecasting model of BP neural network of Xiaosha River artificial wetland.

Estimation of Natural Mortality Coefficient from Fish Abundance and Catch Data Using Virtual Population Analysis(VPA)

Natural mortality coefficient (M) was estimated from fish abundance (N) and catch (C) data using a Virtual Population Analysis (VPA) model. Monte Carlo simulations were used to evaluate the impact of different error distributions for the simulated data on the estimates of M. Among the four error structures (normal, lognormal, Poisson and gamma), simulations of normally dis-tributed errors produced the most viable estimates for M, with the lowest relative estimation errors (REEs) and median mean absolute deviations (MADs) for the ratio of the true to the estimated Ms. In contrast, the lognormal distribution had the largest REE value. Errors with different coefficients of variation (CV) were added to N and C. In general, when CVs in the data were less than 10%, reliable estimates of M were obtained. For normal and lognormal distributions, the estimates of M were more sensitive to the CVs in N than in C; when only C had error the estimates were close to the true. For Poisson and gamma distributions, opposite results were obtained. For instance, the estimates were more sensitive to the CVs in C than in N, with the largest REE from the scenario of error only in C. Two scenarios of high and low fishing mortality coefficient (F) were generated, and the simulation results showed that the method performed better for the scenario with low F. This method was also applied to the published data for the anchovy (Engraulis japonicus) of the Yellow Sea. Viable estimates of M were obtained for young groups, which may be explained by the fact that the great uncertainties in N and C observed for older Yellow Sea anchovy introduced large variation in the corresponding estimates of M.

ROUTE SELECTION IN VIRTUAL CIRCUIT DATA NETWORKS BASED ON GENETIC ALGORITHMS

This paper addresses the problem of selecting a route for every pair of communicating nodes in a virtual circuit data network in order to minimize the average delay encountered by messages. The problem was previously modeled as a network of M/M/1 queues. Agenetic algorithm to solve this problem is presented. Extensive computational results across a variety of networks are reported. These results indicate that the presented solution procedure outperforms the other methods in the literature and is effective for a wide range of traffic loads.

A Virtual Router Cluster System Based on the Separation of the Control Plane and the Data Plane

This paper proposes a virtual router cluster system based on the separation of the control plane and the data plane from multiple perspectives,such as architecture,key technologies,scenarios and standardization.To some extent,the virtual cluster simplifies network topology and management,achieves automatic conFig.uration and saves the IP address.It is a kind of low-cost expansion method of aggregation equipment port density.

医院安全移动应用平台的设计和实现

目的:降低医院移动办公和移动业务场景中的数据安全泄露风险。方法:基于虚拟手机技术,将移动办公和移动业务App实际安装和运行在医院内网的虚拟手机服务器上,而用户手持的移动终端只作为虚拟手机的显示和输入设备,同时部署数据安全网关,实现虚拟手机与移动终端的网络隔离,防止内部数据泄露到外部。结果:用户通过移动终端访问虚拟手机中的移动应用,所有的应用数据都只在虚拟手机中被处理和存储,从而避免数据从移动终端泄露。结论:建设安全移动应用平台可以降低医院移动办公和移动业务场景中的数据安全风险,减少医院对移动终端进行安全管控的要求,有助于推动移动业务的健康发展。

Predicting an Optimal Virtual Data Model for Uniform Access to Large Heterogeneous Data

The growth of generated data in the industry requires new efficient big data integration approaches for uniform data access by end-users to perform better business operations.Data virtualization systems,including Ontology-Based Data Access(ODBA)query data on-the-fly against the original data sources without any prior data materialization.Existing approaches by design use a fixed model e.g.,TABULAR as the only Virtual Data Model-a uniform schema built on-the-fly to load,transform,and join relevant data.While other data models,such as GRAPH or DOCUMENT,are more flexible and,thus,can be more suitable for some common types of queries,such as join or nested queries.Those queries are hard to predict because they depend on many criteria,such as query plan,data model,data size,and operations.To address the problem of selecting the optimal virtual data model for queries on large datasets,we present a new approach that(1)builds on the principal of OBDA to query and join large heterogeneous data in a distributed manner and(2)calls a deep learning method to predict the optimal virtual data model using features extracted from SPARQL queries.OPTIMA-implementation of our approach currently leverages state-of-the-art Big Data technologies,Apache-Spark and Graphx,and implements two virtual data models,GRAPH and TABULAR,and supports out-of-the-box five data sources models:property graph,document-based,e.g.,wide-columnar,relational,and tabular,stored in Neo4j,MongoDB,Cassandra,MySQL,and CSV respectively.Extensive experiments show that our approach is returning the optimal virtual model with an accuracy of 0.831,thus,a reduction in query execution time of over 40%for the tabular model selection and over 30%for the graph model selection.

基于激光雷达点云数据的虚拟室内场景搭建研究

通过搭建虚拟室内场景实现室内视景重构,提出基于激光雷达点云数据的虚拟室内场景搭建技术,采用三维光学面扫描设备采集激光雷达点云数据,采用融合卡尔曼滤波方法实现对虚拟室内场景激光雷达点云数据滤波处理,减少点云数据干扰,提取虚拟室内场景的空间分布域特征量,获取虚拟室内场景中被测量物体的全局标志点,通过逆向设计和三维边界轮廓特征点重构的方法实现对虚拟室内场景散乱点云重组和稀疏性特征点拟合,在拟合曲面采用纹理渲染方法实现对虚拟室内场景重构。仿真结果表明,采用该方法进行虚拟室内场景搭建的激光雷达点云数据重建能力较好,保留原始点云的特征信息较为饱满,能满足较好地重建几何精度场合,重建精度达到了0.98,并且重建时间仅为4.5 h。

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

Cloud computing has gained significant recognition due to its ability to provide a broad range of online services and applications.Nevertheless,existing commercial cloud computing models demonstrate an appropriate design by concentrating computational assets,such as preservation and server infrastructure,in a limited number of large-scale worldwide data facilities.Optimizing the deployment of virtual machines(VMs)is crucial in this scenario to ensure system dependability,performance,and minimal latency.A significant barrier in the present scenario is the load distribution,particularly when striving for improved energy consumption in a hypothetical grid computing framework.This design employs load-balancing techniques to allocate different user workloads across several virtual machines.To address this challenge,we propose using the twin-fold moth flame technique,which serves as a very effective optimization technique.Developers intentionally designed the twin-fold moth flame method to consider various restrictions,including energy efficiency,lifespan analysis,and resource expenditures.It provides a thorough approach to evaluating total costs in the cloud computing environment.When assessing the efficacy of our suggested strategy,the study will analyze significant metrics such as energy efficiency,lifespan analysis,and resource expenditures.This investigation aims to enhance cloud computing techniques by developing a new optimization algorithm that considers multiple factors for effective virtual machine placement and load balancing.The proposed work demonstrates notable improvements of 12.15%,10.68%,8.70%,13.29%,18.46%,and 33.39%for 40 count data of nodes using the artificial bee colony-bat algorithm,ant colony optimization,crow search algorithm,krill herd,whale optimization genetic algorithm,and improved Lévy-based whale optimization algorithm,respectively.

虚拟仿真技术在实验教学中的应用

阐述大数据和虚拟仿真技术的特点,分析其在高职实验实训教学中的优势和作用。结合具体案例,探讨大数据和虚拟仿真技术在改进高职实验实训教学、提高教学效果和学生实践能力方面的应用。

交汇、挑战与应对:大数据技术对人类学民族志的影响

大数据技术的快速发展为研究者提供了更广泛、更丰富的数据资源,其规模性、实时性、多样性和高度可追溯性也为人类学研究带来了全新的视角和方法。本文探讨了大数据对人类学民族志方法的影响,即大数据技术与民族志在本体论、认识论和方法论上均有交汇,使民族志兼具定性与定量的双重特质,大数据技术促进了增强型民族志、网络民族志和线下民族志的发展与创新,拓宽了人类学民族志方法运用的空间。大数据也给传统民族志方法带来挑战,使传统民族志面临技术依赖和数据过载的困扰,过度依赖大数据容易导致对数据背后的人文因素的忽视,大数据“关键问题”策略挑战民族志的客观性与准确性,且大数据难以将人类经验与主观感受完全转换成数据。此外,民族志对大数据技术的运用还面临伦理和隐私保护的问题,如何避免被研究者信息泄露并保护其知情权也是民族志方法中的重点问题。针对这些问题,笔者认为在民族志中需要关注“厚数据”和“小数据”,注重对数据背后的社会和象征意义的深度解释,并严肃对待伦理问题。

数据驱动的机身对接过程仿真技术研究

针对传统机身对接仿真中理论模型偏差较大、有限元模型消耗时间长等问题,开展数据驱动的机身对接过程仿真技术研究。首先通过虚实融合技术,构建六自由度平台运动学模型和机身–调姿平台位姿转换模型,通过数据交互实现仿真数据实时传输从而驱动虚拟模型,利用测量数据和虚拟模型进行了机身对接仿真,确定了对接过程位姿参数,为调姿平台参数修正提供数据基础。之后为提升数据求解效率,根据求解机身变形的有限元模型,计算出变形量并转化成机身位置变化量,将机身姿态角和位置变化量作为输入输出值构建代理模型,并验证方法有效性。最后开发了机身对接过程仿真系统,以机身试验件的对接过程为例,验证了仿真系统的可行性。

高等职业技术学院云机房设计及优势探讨

本文通过分析高等职业技术学院传统机房使用中存在维护工作量大、投资费用高、资源利用低等诸多问题,提出了利用云计算技术来解决传统机房不足之处。通过云机房方案设计,采用分布式存储系统,提出整体架构由基础框架、管理软件、桌面控制器、终端接入设备构成,并总结了云机房部署灵活、运维方便、随时教学、安全高效的优势,最后结合实际分析了云机房网络依赖性高、终端处理能力弱、专业性软件适应性差等问题。

装配机器人的数字孪生虚实同步及抓取方法

针对工业机器人进行装配任务时对物料智能抓取精度差和数据处理难等问题,提出一种基于数字孪生的装配机器人虚实同步及抓取方法。以数字孪生为基础,设计了数字孪生装配机器人虚实同步及抓取架构。通过对多源异构数据的分类实现OPC UA服务器和客户端的信息模型搭建,并以OPC UA通信协议为桥梁进行装配机器人的数据通信,实现虚实同步。通过虚拟机器人对卷积神经网络进行进一步训练,提高对物料的抓取精度。在所开发的机器人数字孪生原型系统中验证所提方法的正确性和有效性,为实现装配机器人的数字孪生虚实同步和精确抓取提供新方案。

网络安全审计中特征数据关联方法的研究

文章提出了一种基于多特征要素的网络安全审计中的特征数据关联方法。该方法以国际移动设备识别码(International Mobile Equipment Identity,IMEI)、国际移动用户识别码(International Mobile Subscriber Identification,IMSI)、移动终端MAC(TERMINAL_MAC)地址三个特征要素为关联因子,通过持续更新完善特征信息串的方式,有效解决了在接入网络的移动终端MAC地址可能发生周期变化的情况下,构建移动终端用户唯一虚拟画像的问题。

基于数字孪生的液压回路虚拟实验系统

基于智能制造过程中数字孪生技术的应用及新工科人才培养数字孪生实践锻炼需求,探索了沉浸式液压回路虚拟实验数字孪生技术在工程教育中的应用。开发了基于数字学生的液压回路数字孪生系统,通过创建物理实体的精确虚拟孪生模型,并实时同步数据,为学习者提供了一个安全、可控且高度互动的学习环境。系统架构整合了物理实体、数据连接映射与孪生模型,实现了节流调速回路的实时数据采集、存储与三维可视化。通过该实验系统进行实验教学,不仅提升了教学效率,还增强了学生对新工科数字孪生技术的理解,并为学习者提供了全新的学习视角和工具。在提高对液压回路原理理解的同时,锻炼了学生解决实际工程问题的能力,为工程教育的创新与进步提供了新的思路和方法。