应急地理信息数据共享关键技术研究

异构数据的共享是应急地理信息服务平台需要重点解决的关键技术之一。该文从共享模式、集成技术、数据组织等三个方面对应急地理信息数据共享进行研究探讨,选择了基于Web服务的数据共享模式、虚拟数据库法的集成技术,并结合地理空间数据的结构特征和使用特点设计了数据组织方式,最后结合地震应急案例,建设了用于地震应急的辅助决策综合库,可为地震应急指挥提供辅助决策支持。     

大数据环境下高校人事档案的管理与利用

在大数据环境下,高校档案管理人员应当重视人事档案管理与应用,不仅要传承以往的优秀经验,还应当创新档案管理方式,重视对数字档案管理工作的研究,逐步制定新的档案管理模式。进而将以往的信息共享逐步转变为信息价值,大大提高高校人事档案管理效率。本文主要针对大数据环境下高校人事档案的管理与应用上的问题进行分析。首先探讨了高校人事档案管理过程中的常见问题与挑战,最后提出了相应的解决办法,以期为有关研究人士提供参考和借鉴。     

确保科技成为“第一生产力”

"科学数据资源,是人类长期以来智慧的结晶、是信息与知识创新的源泉、提高人们生活质量的保证、推动社会发展的重要条件;然而,只有对它实现有效的开发、管理、利用,建立起完善的共享、服务机制,充分发挥科学数据的科学价值、经济价值和社会价值,才能确保科技的‘第一生产力'地位."中国工程院院士、我国著名农业与资源环境信息工程专家孙九林院士这样说道.     

面向智慧城市的数据开放共享平台建设研究

文章以智慧城市建设为背景,针对当前数据整合能力不足、数据利用率低、数据安全问题频发等数据问题,运用数据采集融合、数据治理、数据安全保障等技术,设计一种数据开放共享平台,提升在智慧城市场景下数据采集、数据治理、数据管理、数据开放共享、数据安全管控等方面的能力,提高城市数据资源整合和利用能力,促进城市管理与服务的数字化、智能化。     

数据驱动下共享出行资源配置的双层博弈问题研究

依托互联网技术和共享经济，共享出行成为了一种流行的交通服务模式。在复杂市场环境中，共享企业如何在政府监管下更好地服务用户，非常值得关注。本文考虑共享出行系统中多层级多主体之间复杂的交互关系和利益冲突，构建了以政府为主导的市场资源配置双层规划建模框架，以期实现多主体利益平衡。政府作为上层模型的决策主体，以公共利益为导向规划共享出行行业资源宏观配置；下层模型着重解决共享出行企业如何在政府约束、竞争/合作影响下的自身运营优化问题。为充分利用大数据资源科学地校准双层模型相关参数，本文将大数据分析方法应用于优化模型构建与问题求解中。应用算例分析了政府监管对市场发展的引导作用，通过对比竞争与合作关系下企业的盈利差异，体现企业间合作对互补型企业效益的提升作用。     

大数据环境下档案信息风险治理研究

本文从内部和外部角度分析了大数据环境下档案管理面临的信息安全风险，指出档案信息风险治理存在的问题。从健全大数据环境下档案信息风险管理、强化信息风险治理技术保障、完善档案信息风险治理体制机制的角度，提出大数据环境下档案信息风险治理策略。     

企业财务管理信息化中智能预算管理系统研究

本文从企业财务管理信息化的现状,企业财务管理信息化智能预算管理系统各部分的功能设计,企业财务管理信息化智能预算管理系统各部分的相应模块及其内涵,包括企业预算管理系统编制及变更部分的年度预算编制模块、预算变更管理模块;企业预算管理系统执行及合同部分的预算执行控制模块、预算合同管理模块;企业预算管理系统支付及显示部分的资金支付管理模块、资金实时数据显示模块;企业预算管理系统分析及制证部分的预算分析研究模块、预制凭证管理模块;企业预算管理系统安全及服务部分的预算数据安全管理模块、预算数据智能共享服务管理模块等方面,对基于企业财务管理信息化中的智能预算管理系统进行了探讨和研究。     

浅谈烟草企业建设企业服务总线的好处

企业服务总线是一个企业信息架构的神经中枢,消除不同系统之间的技术差异,让不同的信息系统协调运作,实现不同系统资源的通信和整合。建设企业服务总线,就是要通过规范基础数据管理规范数据交换管理,提高信息集成共享从而为企业管理层和决策层提供及时、真实、准确的业务数据和决策信息。     

关于网络环境下图书馆信息服务工作的思考

随着现代计算机技术和网络通讯技术的产生、发展和普及,形成了信息资源高度开放和高度共享的网络化信息环境,由此开辟了信息交流传递的全新途径。图书馆作为社会重要的信息资源基地,国家信息基础设施和资源的提供者,对信息化、网络化浪潮的冲击更为敏感。因此,为继续维护和强化图书馆的功能和地位,深化服务内容,转变服务方式、拓展服务空间,将是信息环境下图书馆的必要举措。     

电子政务系统的信息集成研究

针对目前我国电子政务信息系统分布式异构的特点所带来的信息共享问题,本文提出了基于CORBA和XML技术构建分布式异构数据集成系统,从而实现网络环境下的政府部门间的信息共享,满足用户对信息集成的需求。     

基于改进自适应最优分割法的风险预警区间模型研究——针对重大突发公共卫生事件

重大突发公共卫生事件，譬如新型冠状病毒疫情，严重危害着世界各国人民的生命安全，风险预警是构建重大突发公共卫生事件风险预警管控体系的关键所在，而其风险预警区间的精准确定是关乎预警等级的关键问题。基于自适应最优分割模型，引入熵值法计算各指标权重，采用多种函数拟合识别函数特征，构建了改进的自适应最优分割模型，定量科学划分了重大突发公共卫生事件风险预警区间。通过结合实际案例，应用Matlab软件进行仿真，验证了预警区间的吻合度，为构建重大突发公共卫生事件风险预警防控提供了理论参考。     

Sortabilities of Partition Properties

Consider the partition of a set of integers into parts. Various partition properties have been proposed in the literature to facilitate the restriction of the focus of attention to some small class of partitions. Recently, Hwang, Rothblum and Yao defined and studied the sortability of these partition properties as a tool to prove the existence of a partition with such a property in a given family. In this paper we determine the sortability indices of the seven most interesting properties of partitions providing a complete solution to the sortability issue.     

Security of Separated Data in Cloud Systems with Competing Attack Detection and Data Theft Processes

Empowered by virtualization technology, service requests from cloud users can be honored through creating and running virtual machines. Virtual machines established for different users may be allocated to the same physical server, making the cloud vulnerable to co‐residence attacks where a malicious attacker can steal a user's data through co‐residing their virtual machines on the same server. For protecting data against the theft, the data partition technique is applied to divide the user's data into multiple blocks with each being handled by a separate virtual machine. Moreover, early warning agents (EWAs) are deployed to possibly detect and prevent co‐residence attacks at a nascent stage. This article models and analyzes the attack success probability (complement of data security) in cloud systems subject to competing attack detection process (by EWAs) and data theft process (by co‐residence attackers). Based on the suggested probabilistic model, the optimal data partition and protection policy is determined with the objective of minimizing the user's cost subject to providing a desired level of data security. Examples are presented to illustrate effects of different model parameters (attack rate, number of cloud servers, number of data blocks, attack detection time, and data theft time distribution parameters) on the attack success probability and optimization solutions.     

An icon-based data image construction method for production data visualization

Visualization takes advantage of human's rapid and flexible pattern recognition capacity to provide a powerful information-processing environment. It is especially suitable for identifying patterns that are difficult, or impossible, to be defined by a mathematical or computer model. This paper presents an icon-based method for constructing images to visualize the abstract, multidimensional production data stored in tables. The method employs a simple, topology-preserving mapping to convert numbers in a table to icons. This mapping strategy not only makes the meaning of the resulted data image easy to understand, but also makes the maximum number of dimensions and data items that could be displayed be virtually unlimited. Visual patterns in the data image are formed by adjacent icons that have similar grey scales. The area sampling techniques could be applied to reduce the size of the data image while still retaining its visual patterns. The proposed visualization method has been applied to several production planning and control activities, including analysing WIP patterns, production indices, and the machine idle cost performance of production plans. These industrial case studies demonstrate that the ability to quickly and easily examine large amount of manufacturing data through visualization is critical for manufacturing companies to understand their performance, discover possible problems and take necessary actions so that they could remain competitive in the market.     

基于流数据挖掘的网络流量异常检测及分析研究

网络流量异常检测及分析是网络及安全管理领域的重要研究内容.本文探讨了网络流量异常的种类、网络流量异常检测的方法,分析了基于传统检测方法在网络流量异常检测应用中存在的问题.并重点对基于流数据模型的网络流量异常检测进行了研究,综述了已有流数据挖掘研究方法在网络流量异常检测中的研究进展.最后,本文对现有研究工作存在的问题及未来的研究方向进行了探讨.     

基于差分进化自动聚类的信用风险评价模型研究

随着风险评价的日益复杂化, 多维度、多时序等不规则的样本数据增加了评估的难度。本文建立信用风险评价的差分进化自动聚类模型, 并将其应用到我国上市公司信用风险评价中。该模型不要求事先知道分类的数据, 相反, 通过群体智能去寻找最优的分区。通过数据仿真, 并与遗传算法、决策树、BP神经网络模型进行信用风险评价的实证对比研究, 结果表明, 该模型能够非常准确的找到数据对应的分区, 大大提高了信用评估的准确性, 降低了风险成本, 对信用风险的管理和控制具有很高的利用价值。     

Fast On-Line/Off-Line Algorithms for Optimal Reinforcement of a Network and its Connections with Principal Partition

The problem of computing the strength and performing optimal reinforcement for an edge-weighted graph G(V, E, w) is well-studied. In this paper, we present fast (sequential linear time and parallel logarithmic time) on-line algorithms for optimally reinforcing the graph when the reinforcement material is available continuously on-line. These are the first on-line algorithms for this problem. We invest O(|V|³|E|log|V|) time (equivalent to (|V|) invocations of the fastest known algorithms for optimal reinforcement) in preprocessing the graph before the start of our algorithms. It is shown that the output of our on-line algorithms is as good as that of the off-line algorithms. Thus our algorithms are better than the fastest off-line algorithms in situations when a sequence of more than (|V|) reinforcement problems need to be solved. The key idea is to make use of ideas underlying the theory of Principal Partition of a Graph. Our ideas are easily generalized to the general setting of polymatroid functions. We also present a new efficient algorithm for computation of the Principal Sequence of a graph.     

A Bayesian Evidence Synthesis for Estimating Campylobacteriosis Prevalence

Stakeholders making decisions in public health and world trade need improved estimations of the burden‐of‐illness of foodborne infectious diseases. In this article, we propose a Bayesian meta‐analysis or more precisely a Bayesian evidence synthesis to assess the burden‐of‐illness of campylobacteriosis in France. Using this case study, we investigate campylobacteriosis prevalence, as well as the probabilities of different events that guide the disease pathway, by (i) employing a Bayesian approach on French and foreign human studies (from active surveillance systems, laboratory surveys, physician surveys, epidemiological surveys, and so on) through the chain of events that occur during an episode of illness and (ii) including expert knowledge about this chain of events. We split the target population using an exhaustive and exclusive partition based on health status and the level of disease investigation. We assume an approximate multinomial model over this population partition. Thereby, each observed data set related to the partition brings information on the parameters of the multinomial model, improving burden‐of‐illness parameter estimates that can be deduced from the parameters of the basic multinomial model. This multinomial model serves as a core model to perform a Bayesian evidence synthesis. Expert knowledge is introduced by way of pseudo‐data. The result is a global estimation of the burden‐of‐illness parameters with their accompanying uncertainty.