Incorporating Domain Knowledge into Data Mining Process:An Ontology Based Framework

With the explosive growth of data available, there is an urgent need to develop continuous data mining which reduces manual interaction evidently. A novel model for data mining is proposed in evolving environment. First, some valid mining task schedules are generated, and then au tonomous and local mining are executed periodically, finally, previous results are merged and refined. The framework based on the model creates a communication mechanism to in corporate domain knowledge into continuous process through ontology service. The local and merge mining are transparent to the end user and heterogeneous data ,source by ontology. Experiments suggest that the framework should be useful in guiding the continuous mining process.

Bayesian framework for satellite rechargeable lithium battery synthesizing bivariate degradation and lifetime data

Reliability and remaining useful life(RUL)estimation for a satellite rechargeable lithium battery(RLB)are significant for prognostic and health management(PHM).A novel Bayesian framework is proposed to do reliability analysis by synthesizing multisource data,including bivariate degradation data and lifetime data.Bivariate degradation means that there are two degraded performance characteristics leading to the failure of the system.First,linear Wiener process and Frank Copula function are used to model the dependent degradation processes of the RLB's temperature and discharge voltage.Next,the Bayesian method,in combination with Markov Chain Monte Carlo(MCMC)simulations,is provided to integrate limited bivariate degradation data with other congeneric RLBs'lifetime data.Then reliability evaluation and RUL prediction are carried out for PHM.A simulation study demonstrates that due to the data fusion,parameter estimations and predicted RUL obtained from our model are more precise than models only using degradation data or ignoring the dependency of different degradation processes.Finally,a practical case study of a satellite RLB verifies the usability of the model.

Spectrum Prediction Based on GAN and Deep Transfer Learning:A Cross-Band Data Augmentation Framework

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.

Towards a Comprehensive Security Framework of Cloud Data Storage Based on Multi Agent System Architecture

The tremendous growth of the cloud computing environments requires new architecture for security services. Cloud computing is the utilization of many servers/data centers or cloud data storages (CDSs) housed in many different locations and interconnected by high speed networks. CDS, like any other emerging technology, is experiencing growing pains. It is immature, it is fragmented and it lacks standardization. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this paper a comprehensive security framework based on Multi-Agent System (MAS) architecture for CDS to facilitate confidentiality, correctness assurance, availability and integrity of users' data in the cloud is proposed. Our security framework consists of two main layers as agent layer and CDS layer. Our propose MAS architecture includes main five types of agents: Cloud Service Provider Agent (CSPA), Cloud Data Confidentiality Agent (CDConA), Cloud Data Correctness Agent (CDCorA), Cloud Data Availability Agent (CDAA) and Cloud Data Integrity Agent (CDIA). In order to verify our proposed security framework based on MAS architecture, pilot study is conducted using a questionnaire survey. Rasch Methodology is used to analyze the pilot data. Item reliability is found to be poor and a few respondents and items are identified as misfits with distorted measurements. As a result, some problematic questions are revised and some predictably easy questions are excluded from the questionnaire. A prototype of the system is implemented using Java. To simulate the agents, oracle database packages and triggers are used to implement agent functions and oracle jobs are utilized to create agents.

Evolution of corporate environmental information disclosure framework of “knowledge and action”: Based on big data background

Cloud accounting is based on the traditional financial work process, the context of big data, and the necessary trend of future corporate accounting development. Its emergence and rapid development will have a fundamental impact on corporate environmental information disclosure. In the big data era of information sharing, companies will have a new understanding of the emergence, balance, and final consideration of social responsibility, and will have new changes in their overall decision-making and information disclosure methods. "Knowing" and "behavior" will be combined on the basis of rational judgment, so that corporate environmental information disclosure is more in line with the overall social development requirements. Based on the background of big data, this article starts with the disclosure of impact factors, footholds, and path choices. It describes the evolution of corporate environmental information disclosure and provides reference suggestions for enterprises to disclose environmental information truthfully and perform social responsibilities.

A Novel Approach to Design Distribution Preserving Framework for Big Data

In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.

Preliminary study of Chinese Antarctic data management in accordance with the international framework

Antarctic data management is the research focus, which the international Antarctic organizations, e.g. Antarctic Treaty Consultative Meeting(ATCM) , Scientific Committee on Antarctic Research(SCAR), and Council of Managers of National Antarctic Programmes(COMNAP) have been paying close attention to and promoting actively. Through the co effort of international Antarctic organizations and member countries concerned in recent years, Antarctic Data Directory Syatem(ADDS) is established as the most important basic programme for development of the international Antarctic data management system. At present, Joint Committee on Antarctic Data Management(JCADM) is responsible for organizing and coordinating the international Antarctic data management, and implementing the project ADDS.In this paper the background on Antarctic data management in time sequence and the structure of international framework are introduced, meanwhile, it is necessary to develop ADDS first of all. The ADDS mainly consists of the two principal parts: National Antarctic Data Center(NADCs) of all the party members and Antarctic Main Directory(AMD), the best available technology for creating ADDS is to make full use of International Directory Network(IDN) and adopt its Directory Interchange Formats(DIF). In the light of the above requirements, combined with Chinese specific situation, the contents, technical and administrative methods on Chinese Antarctic data management are discussed to promote our related work.

Evaluating Water Withdrawals for Regional Water Management Under a Data-driven Framework

With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–2018.Total water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in 2018.Agricultural water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in 2018.Industrial and domestic water usage constantly increased throughout the 1978–2018 period.In 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–2018.The overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–2018.The results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.

A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

This paper proposes a two-step general framework for reversible data hiding(RDH)schemes with controllable contrast enhancement.The first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity(EC),while the second step is used for increasing image contrast.In the second step,some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization(HE),which would lead to the image contrast enhancement.However,for HE,the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are embedded.Therefore,in our proposed framework,contrast over-enhancement is avoided by controlling the degree of contrast enhancement.Since the second step can only provide a small amount of data due to controlled contrast enhancement,the first one helps to achieve a large amount of data without degrading visual quality.Any RDH method which can achieve high EC while preserve good visual quality,can be selected for the first step.In fact,Gao et al.’s method is a special case of our proposed framework.In addition,two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.

Security Framework for Managing Data Security within Point of Care Tests

Point of Care (PoC) devices and systems can be categorized into three broad classes (CAT 1, CAT 2, and CAT 3) based on the context of operation and usage. In this paper, the categories are defined to address certain usage models of the PoC device. PoC devices that are used for PoC testing and diagnostic applications are defined CAT 1 devices;PoC devices that are used for patient monitoring are defined as CAT 2 devices (PoCM);PoC devices that are used for as interfacing with other devices are defined as CAT 3 devices (PoCI). The PoCI devices provide an interface gateway for collecting and aggregating data from other medical devices. In all categories, data security is an important aspect. This paper presents a security framework concept, which is applicable for all of the classes of PoC operation. It outlines the concepts and security framework for preventing security challenges in unauthorized access to data, unintended data flow, and data tampering during communication between system entities, the user, and the PoC system. The security framework includes secure layering of basic PoC system architecture, protection of PoC devices in the context of application and network. Developing the security framework is taken into account of a thread model of the PoC system. A proposal for a low-level protocol is discussed. This protocol is independent of communications technologies, and it is elaborated in relation to providing security. An algorithm that can be used to overcome the threat challenges has been shown using the elements in the protocol. The paper further discusses the vulnerability scanning process for the PoC system interconnected network. The paper also presents a four-step process of authentication and authorization framework for providing the security for the PoC system. Finally, the paper concludes with the machine to machine (M2M) security viewpoint and discusses the key stakeholders within an actual deployment of the PoC system and its security challenges.

Selecting the Six Sigma Project: A Multi Data Envelopment Analysis Unified Scoring Framework

The importance of the project selection phase in any six sigma initiative cannot be emphasized enough. The successfulness of the six sigma initiative is affected by successful project selection. Recently, Data Envelopment Analysis (DEA) has been proposed as a six sigma project selection tool. However, there exist a number of different DEA formulations which may affect the selection process and the wining project being selected. This work initially applies nine different DEA formulations to several case studies and concludes that different DEA formulations select different wining projects. Also in this work, a Multi-DEA Unified Scoring Framework is proposed to overcome this problem. This framework is applied to several case studies and proved to successfully select the six sigma project with the best performance. The framework is also successful in filtering out some of the projects that have “selective” excellent performance, i.e. projects with excellent performance in some of the DEA formulations and worse performance in others. It is also successful in selecting stable projects;these are projects that perform well in the majority of the DEA formulations, even if it has not been selected as a wining project by any of the DEA formulations.

A Quality Assurance Reference Framework for Assessing Educational Data

Digital educational content is gaining importance as an incubator of pedagogical methodologies in formal and informal online educational settings. Its educational efficiency is directly dependent on its quality, however educational content is more than information and data. This paper presents a new data quality framework for assessing digital educational content used for teaching in distance learning environments. The model relies on the ISO2500 series quality standard and beside providing the mechanisms for multi-facet quality assessment it also supports organizations that design, create, manage and use educational content with the quality tools (expressed as quality metrics and measurement methods) to provide a more efficient distance education experience. The model describes the quality characteristics of the educational material content using data and software quality characteristics.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Development of a Framework for Equipment Health Management in the Mining Industries in Zambia

The Zambian mining industry is crucial to the national economy but struggles with inconsistent equipment maintenance practices. This study developed an Equipment Maintenance Management Framework (EMMF) tailored to the industry’s needs. Using surveys, interviews, and on-site visits at eight major mining companies, we identified significant variations in maintenance strategies, CMMS usage, and reliability engineering. The EMMF prioritizes predictive maintenance, efficient CMMS implementation, ongoing training, and robust reliability engineering to shift from reactive to proactive maintenance. We recommend adopting continuous improvement practices and data-driven decision-making based on performance metrics, with a phased EMMF implementation aligning maintenance with strategic business objectives. This framework is poised to enhance operational efficiency, equipment reliability, and safety, fostering sustainable growth in the Zambian mining sector.

数据要素价值释放机制与框架

科学合理的数据要素价值释放体系是推进数字经济、数字中国与数字社会发展,提升新质生产力的基础。基于DIKW模型从数据、信息、知识和智慧的演变逻辑,厘清数据要素价值释放内在机理及难点,围绕“释放数据要素价值,助力新质生产力发展”核心目标,提出“两基础”“三驱动”“四保障”“五途径”基本体系,构建数据要素价值释放多层次框架。其中,“两基础”指数据要素制度体系和数据要素系统设施;“三驱动”涉及供给侧、场内外交易和需求侧;“四保障”强调提升数据质量、加强安全监管、保护数据权益、促进数据流通交易;“五途径”指从数据资源化、数据资产化、数据资本化、数据普惠化到数据均等化,展示数据价值释放的多元路径。通过DIKW模型,数据在从数据到信息、再到知识与智慧的转化过程中,不仅实现价值创造,还通过系统的治理与分析机制,推动价值实现与价值倍增。最终,通过一个核心、两大基础、三大驱动、四维保障和五条途径的多层次框架,实现数据要素价值的高效释放。框架从支撑体系、动力体系、保障体系及转化体系等维度体现数据要素价值释放的作用与机制,保障数据要素价值有效释放,有助于推动新质生产力的发展,为数字经济、数字中国与数字社会长远发展奠定坚实基础。

基于Sync Framework的矿用防爆手机数据同步技术研究与应用

煤矿瓦斯灾害监控预警系统不仅需要采集在线的安全监控信息,而且需要采集井下离线的检测、观测信息,防治煤与瓦斯突出所使用的日常预测、观测信息是瓦斯灾害监控预警重要的数据来源之一。为解决煤矿井下基于Android防爆手机的防突信息采集App与地面基于SQL Server的防突动态信息数据库间双向同步问题,提出一种基于Sync Framework的SQLite与SQL Server数据库慢同步方案,实现了防突信息采集App与防突动态信息数据库按需双向同步,并在河南能源集团演马庄矿进行了现场实验,取得了较好的应用效果。该方案实现了数据同步模块与具体业务数据库及业务管理系统解耦,因此可推广至防爆手机乃至普通Android手机App与SQL Server数据库同步,具有较高的工程研究和应用价值。

同质与差异:基于结构主题模型的美国政府数据开放政策叙事研究

[目的/意义]随着数智时代的到来,政府数据开放愈发受到关注。现有研究从不同维度探讨了开放政府数据政策,但较少关注政策叙事的同质性和差异性。[方法/过程]以美国政府数据开放政策为例,使用结构主题模型识别政策主题,进而运用叙事政策框架从背景、情节、人物和寓意4个维度分析政策叙事,并考察美国联邦政府和各州政府政策叙事的同质性和差异性。[结果/结论]美国政府数据开放政策主要有5种政策叙事,由于经济发展水平、地理位置等原因,不同地区的政策叙事有所不同。