Designing and Implementing an Advanced Big Data Governance Platform

Contemporary mainstream big data governance platforms are built atop the big data ecosystem components,offering a one-stop development and analysis governance platform for the collection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive data.These platforms serve various role members who have internal and external data needs.However,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the construction of big data governance platforms.This paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.

Mapping cultural ecosystem services in mountain forests using mobile phone data

The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the highest(hot spot)and lowest(cold spot)use.Habitat,stand,demographic,topographic and spatial factors affecting the distribution of activity were also analyzed.Two approaches were applied in our research:global and local Moran’s coefficients,and a machine learning technique,Boosted Regression Trees.The results show that 11,503,320 visits to forest areas were recorded in the“Sudety Zachodnie”in 2019.The most popular season for activities was winter,and the least popular was spring.Using global and local Moran’s I coefficients,three small hot clusters of activity and one large cold cluster were identified.Locations with high values with similar neighbours(hot-spots)were most often visited forest areas,averaging almost 200,000 visits over 2019.Significantly fewer visits were recorded in cold-spots,the average number of visits to these areas was about 4,500.The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation.Results of Boosted Regression Trees modeling of visits in forest,using tree stand habitat and spatial factors accurately explained 76%of randomly selected input data.The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography.The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data.It also allows the identification of factors that may influence the distribution of visits in forests.Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.

Cloud services with big data provide a solution for monitoring and tracking sustainable development goals

To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.

Environmental Information Governance Reform in the Era of Big Data--From Information Disclosure to Public Service

The development of environmental information governance includes three phases: providing for oneself,information disclosure,and public service. And then China is in the transition and transformation of environmental information disclosure to the environmental information public service. The core of the transformation is public participation,in the whole procedure of environmental information supply decision making,production,and quality supervision and evaluation,etc. The target path of the environmental information governance reform includes five parts: improvement of public satisfaction,optimizing information disclosure,information quality control,integration of information resources,and multiple supply.

Big Data in Chinese Government Governance: Analysis of Decision-Making Model Innovation and Practice

The 19th National Congress of the Communist Party of China has put forward higher requirements for Chinese government governance. The government governance has developed to a higher stage. Meanwhile, it faces more challenges, like lack of top-level design and information sharing. To develop a government governance decision-making innovation model, we should make good use of big data to mine in the grassroots government data management network. Both the characteristics of the times and the experience of the practice have proven that big data can empower government governance and promote the construction of a service-oriented government.

Big Data Framework for Kenya’s County Governments

Digitalization is transforming governments across the globe. At the national level, down to regional and multiple departments in the public institutions, unprecedented change is occurring exponentially as a result of massive digitalization. Digitalization is compelling governments at all levels to embrace voluminous data and institute appropriate multi-channel platforms to support digital transformation. While this is the case, most governments have been caught unprepared thwarting maximum benefits spurred by digitalization. Inherently, the social media and e-participation tools for generating huge amount of data have convoluted most governments’ appetite in Big Data management. This situation is further compounded with the slow pace of adoption of these technological tools by citizens and the public sectors. For enhanced e-citizen satisfaction and engagement, as well as e-participation processes, public institutions need to promote engagement and collaboration. In view of advancing benefits to their citizens, public institutions need to institute appropriate measures to collect citizen’s data. The information collected is vital for public institutions in actualizing what services the citizens want. Using literature reviews and cases, the authors examine Big Data benefits in counties and propose a Big Data model to improve efficiency of e-governance services and productivity in county governments. The authors demonstrate Big Data framework has the aptitude of molding citizen’s opinion in county decision making process. Better use of e-technologies is shown in the proposed model which illustrates sharing resources among various data analytics sources. Our proposed framework based on Big Data analytics is a viable initiative to progress effectiveness and productivity, strengthen citizen engagement and participation and encourage decision-making in e-governance services delivery in the counties.

Self-Tuning Parameters for Decision Tree Algorithm Based on Big Data Analytics

Big data is usually unstructured, and many applications require theanalysis in real-time. Decision tree (DT) algorithm is widely used to analyzebig data. Selecting the optimal depth of DT is time-consuming process as itrequires many iterations. In this paper, we have designed a modified versionof a (DT). The tree aims to achieve optimal depth by self-tuning runningparameters and improving the accuracy. The efficiency of the modified (DT)was verified using two datasets (airport and fire datasets). The airport datasethas 500000 instances and the fire dataset has 600000 instances. A comparisonhas been made between the modified (DT) and standard (DT) with resultsshowing that the modified performs better. This comparison was conductedon multi-node on Apache Spark tool using Amazon web services. Resultingin accuracy with an increase of 6.85% for the first dataset and 8.85% for theairport dataset. In conclusion, the modified DT showed better accuracy inhandling different-sized datasets compared to standard DT algorithm.

College Basic Development Status Data Management System Based on Data Governance Framework

In the era of big data, data application based on data governance has become an inevitable trend in the construction of smart campus in higher education. In this paper, a set of data governance system framework covering the whole life cycle of data suitable for higher education is proposed, and based on this, the ideas and methods of data governance are applied to the construction of data management system for the basic development status of faculties by combining the practice of data governance of Donghua University.It forms a closed-loop management of data in all aspects, such as collection, information feedback, and statistical analysis of the basic development status data of the college. While optimizing the management business of higher education, the system provides a scientific and reliable basis for precise decision-making and strategic development of higher education.

Big Data Analysis and Optimization Suggestions of Cultural Facilities in Shijingshan District,Beijing City

With the successful holding of Beijing Winter Olympic Games in 2022,the planning and cultural services of Shijingshan District have been reviewed,but the systematic planning theory has not been fully applied.Through the big data research method,the location advantages and disadvantages of Shijingshan District were analyzed,and the distribution of its cultural facilities was defined.Feasible optimization schemes were proposed according to its advantages and disadvantages as well as the experience and conditions of the Winter Olympics.

The Path to Reshaping the Structure and Chain System of the Publishing Industry in the Era of Big Data

Today,we are living in the era of“big data”where massive amounts of data are used for quantitative decisions and communication management.With the continuous penetration of big data-based intelligent technology in all fields of human life,the enormous commercial value inherent in the data industry has become a crucial force that drives the aggregation of new industries.For the publishing industry,the introduction of big data and relevant intelligent technologies,such as data intelligence analysis and scenario services,into the structure and value system of the publishing industry,has become an effective path to expanding and reshaping the demand space of publishing products,content decisions,workflow chain,and marketing direction.In the integration and reconstruction of big data,cloud computing,artificial intelligence,and other related technologies,it is expected that a generalized publishing industry pattern dominated by virtual interaction will be formed in the future.

Role of Trust, Privacy Concerns and Data Governance in Managers’ Decision on Adoption of Big Data Systems

This paper investigates the role of trust, privacy concerns, and data governance on managers’ intention to use big data systems. In literature, trusting beliefs, such as functionality, helpfulness, and reliability were found to be antecedent of trust in technological artifacts. Notice, access, choice, and security principles were found to be crucial in eliminating privacy concerns. On the other hand, this paper focuses on data storage and data collection which have been significant criterion for managers in evaluating companies’ data governance policies. A model depicting the relationships amongst all these factors and their relation to users’ intention to adopt big data systems and a scale was proposed in the paper.

Open Source Initiatives for Big Data Governance and Security:A Survey

With the rapid development of lnternet technology, the volume of data has increased exponentially. As the large amounts of data are no longer easy to be managed and secured by the owners, big data security and privacy has become a hot issue. One of the most popular research fields for solving the data security and data privacy is within the scope of big data governance and security, In this paper, we introduce the basic concepts of data governance and security. Then, all the state-of-the-art open source frameworks for data governance and security, including Apache Falcon, Apache Atlas, Apache Ranger, Apache Sentry and Kerberos, are detailed and discussed with descriptions of their implementation principles and possible applications.

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

Research on Governance Structure of Big Data of Civil Aviation

Facing with the enormous data scale of the civil aviation industry, the vital issue that enterprises must consider is how to manage and make use of data to play its value. In this study, the content of the governance of big data of civil aviation is analyzed and this paper put forward the specific governance of big data resources of civil aviation to provide relevant support of the governance of big data resources of civil aviation.

Cloud Computing and Big Data: A Review of Current Service Models and Hardware Perspectives

Big Data applications are pervading more and more aspects of our life, encompassing commercial and scientific uses at increasing rates as we move towards exascale analytics. Examples of Big Data applications include storing and accessing user data in commercial clouds, mining of social data, and analysis of large-scale simulations and experiments such as the Large Hadron Collider. An increasing number of such data—intensive applications and services are relying on clouds in order to process and manage the enormous amounts of data required for continuous operation. It can be difficult to decide which of the many options for cloud processing is suitable for a given application;the aim of this paper is therefore to provide an interested user with an overview of the most important concepts of cloud computing as it relates to processing of Big Data.

Research on the Law of Garlic Price Based on Big Data

In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price,the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed,and the application mode of multidisciplinary in the agricultural industry is discussed.On the basis of the big data platform of garlic industry chain,this paper constructs a Garch model to analyze the fluctuation law of garlic price in the circulation link and provides the garlic industry service from the angle of price fluctuation combined with the economic analysis.The research shows that the average price rate of the price of garlic shows“agglomeration”and cyclical phenomenon,which has the characteristics of fragility,left and a non-normal distribution and the fitting value of the GARCH model is very close to the true value.Finally,it looks into the industrial service form from the perspective of garlic price fluctuation.

XML Web Services电子政务异构平台的设计

针对当前电子政务大规模集成的应用需求,结合Web Services的发展状况,基于XML语言,实现一个跨平台、跨系统、跨应用、与语言无关的技术服务架构,从而建立起以Web服务和权限流为核心的电子政务系统异构平台上应用之间的数据共享,最终实现跨系统的协同工作。同时,对电子政务平台上数据的安全性进行了分析,消息在传递过程中进行加密处理。最后对Web Services的应用和集成技术进行了展望。