An Application of a Multi-Tier Data Warehouse in Oil and Gas Drilling Information Management

Expenditure on wells constitute a significant part of the operational costs for a petroleum enterprise, where most of the cost results from drilling. This has prompted drilling departments to continuously look for ways to reduce their drilling costs and be as efficient as possible. A system called the Drilling Comprehensive Information Management and Application System (DCIMAS) is developed and presented here, with an aim at collecting, storing and making full use of the valuable well data and information relating to all drilling activities and operations. The DCIMAS comprises three main parts, including a data collection and transmission system, a data warehouse (DW) management system, and an integrated platform of core applications. With the support of the application platform, the DW management system is introduced, whereby the operation data are captured at well sites and transmitted electronically to a data warehouse via transmission equipment and ETL (extract, transformation and load) tools. With the high quality of the data guaranteed, our central task is to make the best use of the operation data and information for drilling analysis and to provide further information to guide later production stages. Applications have been developed and integrated on a uniform platform to interface directly with different layers of the multi-tier DW. Now, engineers in every department spend less time on data handling and more time on applying technology in their real work with the system.

What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.

Efficient Bayesian networks for slope safety evaluation with large quantity monitoring information

New sensing and wireless technologies generate massive data. This paper proposes an efficient Bayesian network to evaluate the slope safety using large-quantity field monitoring information with underlying physical mechanisms. A Bayesian network for a slope involving correlated material properties and dozens of observational points is constructed.

Government Management Innovation in the Age of Big Data

Both opportunities and challenges are currently faced by government management innovation in the age of ＂big data＂. Traditionally, relative studies view the management of governments as the effective means to improve governmental services, without really understanding the structural influence of big data and network technology on governmental mode of thinking. Against such backdrop, this paper tries to conduct critical analysis based upon traditional outcomes in this regard, trying to make full use of the function of big data technology. With these efforts, this paper contributes to the building of an interaction theory that could promote transparency of information and customization and segmentation of the policies. By constructing a mode in which management could be carried out based on the law of big data, by building an information management system in which balance could be achieved between responsibility and freedom, by promoting the rebalancing among public power, online civil society and civil rights, the innovation of governmental management would be achieved.

Best Practices and Innovative Scenarios of Integrated Patient Data Management to Improve Continuity of Care and Scientific Research

Clinical data have strong features of complexity and multi-disciplinarity. Clinical data are generated both from the documentation of physicians＇ interactions with the patient and by diagnostic systems. During the care process, a number of different actors and roles （physicians, specialists, nurses, etc.） have the need to access patient data and document clinical activities in different moments and settings. Thus, data sharing and flexible aggregation based on different users＇ needs have become more and more important for supporting continuity of care at home, at hospitals, at outpatient clinics. In this paper, the authors identify and describe needs and challenges for patient data management at provider level and regional- （or inter-organizational-） level, because nowadays sharing patient data is needed to improve continuity and quality of care. For each level, the authors describe state-of-the-art Information and Communication Technology solutions to collect, manage, aggregate and share patient data. For each level some examples of best practices and solution scenarios being implemented in the Italian Healthcare setting are described as well.

Neighborhood-Based In-Network Caching for Information-Centric Networks

The current Internet is based on host-centric networking, and a user needs to know the host address before reaching a data target in the network. The new architecture of information-centric networking (ICN) facilitates users to locate data targets by giving their data names without any information about host addresses. In-network caching is one of the prominent features in ICN, which allows network routers to cache data contents. In this paper, we emphasize the management of in-network cache storage, and this includes the mechanisms of cache replacement and cache replication. A new cost function is then proposed to evaluate each cache content and the least valuable content is evicted when cache is full. To increase cache utilization, a cooperative caching policy among neighboring routers is proposed. The proper network locations to cache data contents are also discussed in the paper. Experimental results show the superiority of the proposed caching policy than some traditional caching polices.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

A Self-Organizing Memory Neural Network for Aerosol Concentration Prediction

Haze-fog,which is an atmospheric aerosol caused by natural or man-made factors,seriously affects the physical and mental health of human beings.PM2.5(a particulate matter whose diameter is smaller than or equal to 2.5 microns)is the chief culprit causing aerosol.To forecast the condition of PM2.5,this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM2.5.Since the meteorological data and air pollutes data are typical time series data,it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network(SSHL-LSTMNN)containing memory capability to implement the prediction.However,the number of neurons in the hidden layer is difficult to decide unless manual testing is operated.In order to decide the best structure of the neural network and improve the accuracy of prediction,this paper employs a self-organizing algorithm,which uses Information Processing Capability(IPC)to adjust the number of the hidden neurons automatically during a learning phase.In a word,to predict PM2.5 concentration accurately,this paper proposes the SSHL-LSTMNN to predict PM2.5 concentration.In the experiment,not only the hourly precise prediction but also the daily longer-term prediction is taken into account.At last,the experimental results reflect that SSHL-LSTMNN performs the best.

Application of Informatics for the Enhanced Management of Robotic Operations

In the past, the proposed concept-the application of informatics for robot operations-was difficult to implement due to technological limitations. With the advancement of ICT technologies, the production robots can be integrated within the information network, and the critical data can be processed to reveal the very important information. From the information, better managerial decisions are expected that will ultimately contribute to the organizations’ efficiency and competitiveness. In this context, this study explains the merits of quality informatics in the domain of networked robots that are the essential tools for the global production companies. Even though this study merely presents the outcome of robot joint replacement period estimation, the foreseeable advantages of using the proposed method can be numerous. The future environment of global business will even push the boundaries of multinational, distributed production systems, and accelerate the implementation at a faster rate. Accordingly, the brand value of the product is expected to be greater than the country of the manufacturing. The survival of the company will be dependent on the optimized quality level that is encompassing the entire distributed production systems located throughout the whole. Under such circumstances, it can be foreseeable that the innovative ideas and the smart application of advanced technologies should be the priority.

Methods for the Efficient Energy Management in a Smart Mini Greenhouse

To solve the problem of energy efficiency of modern enterprise it is necessary to reduce energy consumption.One of the possible ways is proposed in this research.A multi-level hierarchical system for energy efficiency management of the enterprise is designed,it is based on the modular principle providing rapid modernization.The novelty of the work is the development of new and improvement of the existing methods and models,in particular:1)models for dynamic analysis of IT tools for data acquisition and processing(DAAP)in multilevel energy management systems,which are based on Petri nets;2)method of synthesis of DAAP tools in energy efficiency management information systems(EEMIS)of the enterprise which provides a reduction in hardware and time costs from 10%to 40%;3)method of conflict-free data exchange determining the minimum memory speed for the synthesis of realtime exchanges,it reduces the cost and energy consumption;4)method of calculating the signal of postsynaptic excitation of neural elements decreases the processing time of technological data two or more times.The proposed methods,models and tools have been tested while implementing the EEMIS of the intelligent mini-greenhouse,as a result,energy efficiency has increased by 12%-25%(depending on season and peculiarities of growing plants).

信息化测震数据管理平台的设计与实现

依托公共安全信息化工程,测震台网数据管理平台在数据资源池环境下,采用分层架构和模块化开发,实现了实时流波形接入和存储、站网基础信息管理、常规数据产品产出、针对特定地震的产品产出、波形数据质量分析等测震台网核心业务的信息化。通过平台的接口和界面,事件波形处理与服务、数据质量分析、震源参数基础数据库建设等测震台网业务取得了长足进步。该平台具有业务集成性、功能实用性、资源共享性和技术先进性等特点,有效提升了测震台网信息化水平,为测震台网现代化发展发挥了积极作用。

基于知识与AW-ESN融合的烧结过程FeO含量预测

氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标,烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义.然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战,为此,提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法.首先,针对烧结过程热状态参数缺失的问题,建立烧结料层最高温度分布模型,实现基于料层温度分布特征的FeO含量等级划分;其次,针对烧结过程参数波动频繁的问题,提出基于核函数高维映射的多尺度数据配准方法,有效抑制离群点的影响,提升建模数据的质量;最后,针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题,将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合,建立DK-AWESN模型,有效提升复杂工况下FeO含量的预测精度.现场工业数据试验表明,所提方法能实时准确地预测烧结过程FeO含量,为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.

人工神经网络在实现猪精准饲养模式中的应用研究进展

目前,对集约化猪养殖中产生的大量生产数据的高效分析与合理运用是推动我国生猪产业精准化和智能化发展的关键步骤。人工神经网络(artificial neural network,ANN)的自学习、联想存储、高速寻找优化解等特征在进行大数据信息处理时具有显著优势,在猪生产领域具有极高的应用潜力。笔者以具有单隐藏层的三层误差反向传播前馈人工神经网络(back propagation-aritificial neural network BP-ANN)为代表,对ANN的结构特点及其在猪行为与健康监测、胴体性状与肉品质估测和猪生长速率调控与日粮原料营养价值预测等方面的应用成果进行综述,从生产管理和营养调控两方面展望ANN在未来猪精准饲养模式中的应用前景,为实现科学化、智能化和数字化管理模式下的猪精准养殖业提供理论参考。

量子模糊信息管理数学模型研究

为了高效处理大数据所具有的复杂性和不确定问题,将“不确定性问题+直觉模糊集理论+量子计算”交叉融合,构建基于直觉模糊集理论的量子模糊信息管理数学模型。为了验证该模型的可行性、合理性和有效性,设计了不确定性环境下基于参数化量子线路的量子模糊神经网络仿真实验。实验结果表明,基于该模型的量子模糊神经网络模型能更客观、准确、全面地反映不确定性问题中各对象所蕴含的知识信息,从而提高算法处理大数据的准确性。

多平台融合管控一体化系统设计

为了实现中小企业生产层和管理层之间的信息共享、提高企业生产效率,提出适用于中小企业的管控一体化设计思想。以某中小型污水处理企业为例,调查企业背景、分析企业生产现状,并明确企业对生产管理、水质检测和现场控制的需求。借鉴制造执行系统(MES)的思想提出系统框架。基于网络通信和数据管理技术,设计系统架构。结合多种软件平台定制化设计,开发了一套管控一体化系统。该系统对原本孤立的管理平台、检测平台和处理平台进行融合,实现污废生产调度任务和工艺参数、第三方的独立水质监测数据、全生产过程测控数据的联通和共享。该系统自投入运行以来,显著提高了企业管理效率和自动化水平。该系统的设计为中小企业结合生产实际,开发基于异构平台的数据集成融合系统以实现管控一体化探索了新的开发模式。

基于大数据分析的网络信息安全态势预测方法

网络中存在着各种恶意攻击,会导致用户个人信息的泄露,威胁用户的网络安全。为此,提出基于大数据分析的网络信息安全态势预测方法。采集网络安全数据信息,通过关联分析对其进行归一化处理,以此计算每项安全措施的风险值。在网络安全事件风险值的基础上,结合大数据分析计算网络安全态势的预测值,从而实现基于大数据分析的网络信息安全态势预测。实验结果表明,所提方法的平均网络信息安全态势预测时间仅为22.117 s,说明所提方法能够提高网络信息安全态势预测效率。

计算机信息管理技术在维护网络安全中的应用路径

随着互联网技术的迅速发展,网络安全问题愈发严重,计算机信息管理技术是保护网络安全的重要手段。本文探讨了计算机信息管理技术在维护网络安全中的有效路径,着重分析了数据加密、防火墙构建、身份认证等技术在维护网络安全中的应用方法,为提高网络安全防护能力提供了理论支持和实践指导。

计算机应用信息技术与网络安全管理

文中对计算机应用信息技术与网络安全管理展开了研究,介绍了计算机应用信息技术的发展概况,包括数据加密解密技术、计算机网络结构和通信协议、虚拟化技术在信息技术中的应用等,以讨论网络安全管理的重要性和面临的挑战。另外,分析了访问控制与身份验证技术、漏洞扫描与安全漏洞修复、日志监测与事件响应等涉及网络安全管理的内容和方法。

基于大数据分析的网络安全威胁检测与防范研究

本文探讨了基于大数据分析的网络安全威胁检测与防范方法,通过分析基于大数据分析的信息安全技术的原理和优缺点,阐述网络安全威胁检测与防范的关键技术,提出利用大数据分析技术提升网络安全威胁检测和防范能力的策略,从数据采集与处理、特征提取与选取、模型构建与培训、威胁探测与预防4个方面,对大数据环境下的网络安全威胁进行了研究,总结了基于大数据分析的网络安全威胁检测与防范的有效手段。本研究旨在为网络安全管理提供更加高效、智能的解决方案,应对不断演变的网络威胁。

大数据时代计算机网络安全技术探讨

进入大数据时代,信息数据已经成为重要的战略性资源,不仅关系着社会生产经济的提升,同时也影响着公民个人的隐私安全。网络开放的环境在带来便利的同时,也存在着巨大的隐患。为保证信息数据的安全性,必须要不断加强对计算机网络安全技术的研究,不断对技术手段进行更新,提高对外部非法攻击的抵抗力,减少甚至避免信息丢失、损坏等问题的发生,将信息数据的作用更好地发挥出来。该文从大数据时代的特征出发,对计算机网络安全技术的发展以及应用进行探讨。