Research on Public Management Application Innovation Based on Spark Big Data Framework

Liu, Zhi

doi:https://doi.org/10.1155/2022/3797050

Mathematical Problems in Engineering

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Abstract Introduction Experimental Results and Discussion Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

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Big Data Modelling of Engineering and Management 2022

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Research Article | Open Access

Volume 2022 | Article ID 3797050 | https://doi.org/10.1155/2022/3797050

Research on Public Management Application Innovation Based on Spark Big Data Framework

Zhi Liu¹

Academic Editor: Wen-Tsao Pan

Received11 Mar 2022

Revised21 Apr 2022

Accepted25 Apr 2022

Published17 May 2022

Abstract

Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to the public management of Beijing. The results indicate that the public management platform based on Spark big data framework can improve the public management level of the city and help to build an intelligent city.

1. Introduction

Under the new situation that China attaches great importance to the modernization of governance system and governance capacity, strengthening and improving public management has risen to the strategic level, especially optimize and improve the public management model, promote the construction of public management system, and strive to improve the level of public management [1, 2]. From the current overall operation of public management, although it has made progress compared with the past and shows a good trend, the pertinence, systematicness, and integration of public management are not strong according to the research and analysis according to high standards, especially in promoting the effective integration, connection, and interaction between big data technology and public management, which directly leads to a series of problems that cannot be ignored [3]. In this regard, public management departments and managers should deeply understand the effect of the big data about public management, conduct the analysis and demonstration on the prominent problems faced by the Spark big data, find out the existing weak links, and take practical and effective measures to apply big data technology scientifically, systematically, and widely about the public management [4].

The technical dilemma of public management mainly includes three aspects: first, the confirmation technology of public management is imperfect. Public management theory holds that public value is a collection of public preferences, which puts forward new requirements on the technical level to a certain extent. Public preference is an abstract concept, which requires the development of a technology that can effectively detect the degree of public preference, including the public’s potential or existing preference [5]. Even if this technology exists, the selection of target groups and topics has accurately communicated the problems they want to understand to the public, which need to be further solved and discussed. In the process of urban construction, the key to success is to identify and achieve the common will of the public. However, due to the obstacles of identification technology, we may ignore important parts so that the effect of creating public value cannot be maximized [6, 7].

Second, the evaluation technology of public value is not perfect. As the big data technology develops, and the improvement of public policy evaluation technology continues in the public management, the evaluation of public value is still immature, and public value cannot be fully expressed. Especially for those abstract public values, such as government integrity and procedural justice, although we can know its importance, it is difficult to analyze them through which data [8–10]. Even if the relevant data are collected, the authenticity and reliability of the data cannot be guaranteed, which requires our continuous improvement and development in practice.

Third, the scale that public value can be used as a performance measurement is not perfect. Government performance management based on public value is a new paradigm of government performance governance, which needs to be careful about the value orientation of the construction of government performance evaluation system, including people-oriented, scientific development, serving the public, five in one, which are consistent with the thoughts and propositions of public management value [11–14]. In this process, the construction of government evaluation system, the design of evaluation model, and the selection of evaluation indicators all need technical support.

Recently, big data has been valued by various fields and industries, showing a trend of rapid development, we should deeply understand the impact of big data, which could achieve the good results through the big data, and then promote the reform and innovation of public management [15]. In this regard, public management departments and managers should deeply understand the impact of big data technology and take effective measures to promote the application of big data technology in public management.

By the analysis of the impact of big data technology on public management, it is highlighted that big data is conducive to promoting the accuracy of public management so as to maximize the scientific, healthy, and sustainable development of public management [16, 17]. For example, some places use big data technology to carry out investigation, research, analysis, and demonstration. Each link has been well known, so it is more targeted in the process of carrying out public management. Not only the overall level of public management has been significantly improved, but also the efficiency of public management has been greatly improved and recognized in all aspects [18]. The combination of “online” and “offline” has become an important development direction. It also requires public management departments and managers to adapt to the needs of the situation and vigorously strengthen the construction of public management platforms and carriers. For example, some public management departments have developed a relatively perfect public management system in the process of carrying out their work, the “one-stop” service system has been increasingly improved, and the pertinence and characteristics of public management have been strengthened [19]. At the same time, they have also expanded the field and scope of public management, promoted the effective connection between public management and social management, and made public management more systematic better coordination and effectiveness. Due to the strong openness of big data technology and the use of big data technology to carry out data collection, sorting, and analysis, it can effectively promote “data sharing,” which is important in promoting the scientificity of public management.

The characteristic of big data is that it is highly transparent to programmers and allows parallelization of algorithms in a simple and comfortable way. The algorithm to be parallelized only needs to specify two stages, each stage includes input and output, and then group all associated intermediate values and lists through data analysis to realize the access and output of any machine. Spark is a new big data processing framework, which aims to solve the shortcomings of big data processing. The framework proposes a set of memory conversion beyond the standard, which aims to process data faster in a distributed environment. This is a special type of data structure used to perform parallel computing in a transparent manner. These structures persist, reuse, and cache results. In addition, it manages partitions to optimize data placement and uses a wide range of conversion operations. These functions of Spark framework make it a useful framework for big data processing. Generally speaking, Spark framework is a better alternative framework [20]. It is more effective in batch processing and interactive processing, and its performance is better than MapReduce. Spark programming interface is based on a data framework called resilient distributed datasets (RDDs), which is a read-only collection of data objects distributed in the cluster. Therefore, Spark distributed computing framework can be well applied to the analysis and mining of big data.

Public management analysis and public management application prediction are important in the public management process. For the strategies of public management, Abid and Jemili [21] used Spark big data strategy to conduct data modeling and used clustering technology to solve the application problem of public management. Batova et al. [22] proposed a public management analysis method based on data mining, which uses big data statistical analysis to study public management measures between different cities. For the prediction of public management application, Wu and Xiao [23] proposed an innovative method of public management based on spark big data statistics. Coto-Millán et al. [24] constructed an XGBoost prediction model, which achieved good prediction accuracy for public management measures between different cities.

Therefore, the article proposes an algorithm based on Spark big data framework. Through the construction and analysis of Spark big data framework algorithm, the dataset is vertically arranged with the idea of vertical dataset, which is used to calculate the support of item set cardinality between public management in different cities. At the same time, the default data are used to calculate the itemset between candidate cities, which can obtain the whole big data framework. In addition, in order to reduce the number of candidate item sets and improve the accuracy, the data need to be filtered and analyzed. Finally, taking resident income, happiness index, urban planning, and ecological environment as indicators of public management, this paper makes a detailed study on the application of public management between different cities based on Spark big data framework algorithm so as to provide theoretical and experimental support for public management services between cities.

2. Construction of Big Data Framework Model Based on Spark

2.1. Determination of Indicators

Public management theory can suggest the public service behavior between cities and better express the life satisfaction of residents. On the basis of synthesizing the existing indicators, four typical time-domain characteristics of public management are established, namely residents’ income, happiness index, urban planning, and ecological environment. Based on the above indicators, cluster analysis is carried out on the public management behavior between cities.

In the above index system, residents’ income and happiness index can effectively measure the living standards of urban residents within a specified time and then indirectly reflect the level of public management among cities. Urban planning and ecological environment can effectively represent the promotion degree of public management to urban image.

The weight of each index in public management service has a great impact on the analysis results. Its basic idea is to objectively set the index weight value by using the function of the index to urban public management. The process is as follows: First, we set the initial weight vector for each indicator [25]:where λ1, λ2, λ3, and λ4 represent resident income, happiness index, urban planning, and ecological environment, respectively.

In the process of clustering, when a new cluster center is generated, the contribution of each index to the cluster center is counted:where C_j represents the index value corresponding to the cluster center, X_rand represents the load value randomly selected in the corresponding cluster and the relative relationship between the four indicators, which can better characterize the contribution of each indicator, and z represents the number of clusters.

According to the entropy change principle of big data and the weight data in the index, the data of the ith index is calculated as follows:

2.2. Cluster Analysis of Public Management Services

Spark big data framework clustering analysis does not need to specify the number of clusters in advance and can adaptively generate the clustering centroid according to the data characteristics. It is a typical unsupervised clustering method. The specific implementation process of Spark big data framework is introduced as follows:where y (i, k) represents the kth attribute value of the ith data, and m represents the number of attributes.

Then, the reference degree is set based on the similarity matrix. The reference degree refers to the diagonal value of the similarity matrix. If the reference degree of the similarity matrix is greater, the probability that the starting data point is called the final iterative centroid is higher, and the number of centroids at the end of clustering will be more. R (i, j) is defined to represent the degree to which the data point x_k is suitable as the ith cluster center, that is, the degree of attraction. Definition A (i, j) represents the degree to which the ith cluster center selects data point x_k as its centroid, that is, the degree of attribution. The process of Spark big data framework clustering is not to search the attraction and attribution from the input data but to update the clustering centroid iteratively [26, 27].

The above iteration speed can be adjusted by setting the damping coefficient. In the iterative process, for data points x_i and x_k, if R(i, k) + A(i, k) is the maximum value of R(i, j) + A(i, j)，j = 1, 2, ⋯, N, then data point x_k is determined as the clustering center of data point x_i.

Based on the above competitive iteration, Spark big data framework clustering can obtain the attribution of each sample point and finally realize data clustering. However, the complexity of Spark big data framework clustering algorithm is high, and it is difficult to quickly give analysis results for public management data between cities [28]. Therefore, this paper improves the Spark big data framework clustering, reduces the dimension of the load curve through the load index, and adjusts the reference calculation method to improve the calculation speed of the Spark big data framework clustering similarity matrix so as to meet the analysis needs of public management big data users. The improved calculation method of similarity between data points is [29] as follows:where d_dij represents the Euclidean distance between load curves i and j, d_tij represents the Euclidean distance between load curves i and j, and α represents the weight coefficient.where p_m is the median of the value on the nonprincipal diagonal of the similarity matrix, δ represents the search threshold. What is more, δ > 0 and δ < 0 mean forward search and backward search, respectively.

In order to improve the computational efficiency of Spark big data framework clustering algorithm, DB index is used as the basis to judge the convergence of the algorithm. And DB_min is the minimum value of DB index in the current iteration.

Where n represents the number of clusters, W_i and W_j represent the Euclidean distance from the data points in class i and class j to their cluster centers, respectively, and C_ij represents the Euclidean distance between the cluster centers of class i and class j.

In my opinion, the smaller the DB index, the better the intraclass aggregation, and the lower the interclass similarity, which means the higher the clustering quality.

In general, through the above establishment of spark based big data framework, it can effectively predict the service level and facilities of public management. The specific flow of Spark based big data algorithm is shown in Figure 1.

3. Public Management Service Platform Based on Spark Big Data Framework

3.1. Platform Framework Design

According to the public management facilities between different cities and the development of big data technology, a public management service platform based on Spark big data framework is established, as shown in Figure 2.

Among them, the function of the underlying resource management is to construct the software and hardware resources of the whole platform and be responsible for the storage of big data of urban public management. The middle tier data processing is the core of the whole platform, which is responsible for the preprocessing of various data collected by public management. The focus is to analyze the public management services between cities through the proposed Spark big data model. The function of the top-level result display layer is human-computer interaction, which is responsible for receiving various user instructions and displaying various data processing results.

3.2. Key Technologies of the Platform

3.2.1. HBase Inline Storage Architecture

HBase is a column storage database system based on the HDFS distributed file system. HBase storage architecture is a typical master-slave storage management structure, which is composed of one master node and multiple slave nodes. The master node is the central server that stores files, maintains the bottom byte space of the system, and is responsible for storing the basic node data of the whole platform. The child node is responsible for local data storage and data verification, and sends data requests to the master node. Using multinode concurrent access, the data throughput of the platform is significantly improved. In addition, the base storage architecture is equipped with a copy redundancy backup mechanism to ensure the reliability of massive platform data and has a high fault tolerance rate.

3.2.2. MapReduce Computing Framework

MapReduce computing framework is a distributed program implementation framework suitable for massive data. It can be used in conjunction with HBase column storage architecture to jointly realize the calculation and analysis of Spark model in public service big data. The main processing steps of MapReduce computing framework include split, map, shuffle, and reduce. The split process and shuffle process are automatically completed by the MapReduce system architecture, and the map process and reduce need to design and implement the application in combination with the service level in public management.

4. Experimental Results and Discussion

4.1. Innovative Public Management Ideas

Ideas determine the way out. To promote the reform and innovation of public management in the context of Spark big data framework, the most important thing is to learn to use data thinking to carry out public management, strive to make greater breakthroughs in public management, and then promote public management into the track of innovative development. In the specific implementation process, local governments and public management departments should deeply understand the impact of Spark big data technology on public management, especially seize a new round of development opportunities, and strive to achieve greater breakthroughs in the application of Spark big data technology. More importantly, due to the strong openness of Spark big data technology and more and more channels for the public to obtain information, how to adapt to the needs of the development of the situation and vigorously promote the effective combination of public services and public needs is a major issue that governments et al. levels and public management departments must attach great importance to.

This paper selects Beijing as a public management application demonstration city to study its public management application based on Spark big data framework. Figure 3 shows the intercity public management service information obtained after Spark big data innovation. It can be seen that when the time is 60-90 days, the weight of residents’ income and urban planning will be larger, while the weight of happiness index and ecological environment will be more stable, which shows that the innovation platform of Spark big data framework will help to improve Beijing’s public management services, especially the impact on residents’ income and urban planning. Therefore, strengthening innovative public management ideas based on Spark big data framework has a great impact on the urban service quality of Beijing.

4.2. Improving the Public Management System

Improving and perfecting the public management system based on Spark big data framework is important in promoting the reform, innovation, and development of management. In this regard, local governments should deeply understand the effect of Spark big data technology on public management, and Spark big data technology and other related technologies are used to continuously optimize and improve the public management system on this basis. In the specific implementation process, we should use Spark big data technology to build a public management system combining “online” and “offline,” focus on giving full play to the positive role of all aspects, and strengthen the effective coordination and cooperation between public management departments, such as reducing the cost of public management to the greatest extent by strengthening “data sharing.” In the process of perfecting the public management system, public management needs more attention to the application of information technology, especially the Internet plus public management mode. We need to intensify our efforts in the application of Spark big data technology and also integrate effectively in the fields of information technology, network technology, Internet of things, AI technology, and e-commerce technology.

The public management innovation system based on Spark big data framework is shown in Figure 4. It can be seen that with the gradual improvement of public management, residents’ income, happiness index, urban planning, and ecological environment show a gradual upward trend. The happiness index is the highest, followed by residents’ income and ecological environment, and urban planning is the lowest, which shows that after using Spark big data to improve and innovate the public management system, Beijing’s public management has been improved to a certain extent, and helps to improve Beijing’s urban comprehensive level.

4.3. Integrating Public Management Methods

The openness, integration, and strategic functions of Spark big data are very powerful. Therefore, Spark big data technology can be used to vigorously promote the integration of public management methods so as to play a role in all aspects and continuously improve the overall level of public management. For example, public management departments can use Spark big data to conduct in-depth investigation and analysis, understand and master the real situation, and strengthen the effectiveness of public goods and resource management on this basis. In the process of integrating public management methods, it is also necessary for public management to adopt diversified methods to organize and implement, and strive to make public management more targeted and characteristic.

Figure 5 shows the implementation rate of integrated public management based on Spark big data framework. We can see when the public management of Spark big data framework is integrated, the urban realization rate of ecological environment and urban planning is high, while the realization rate of residents’ income is the lowest, which shows that after the integrated public management method of Spark big data is adopted, the ecological environment and urban planning can be greatly improved and the urbanization level of Beijing can be improved. However, the improvement of residents’ income and happiness index is less. The improvement of residents’ living standards in Beijing is limited. It is urgent to carry out other integrated public management methods to improve all indicators so as to improve residents’ life experience and happiness in Beijing.

4.4. Optimizing Public Management Platform

To promote the reform and innovation of public management in the context of Spark big data, we should also strengthen the optimization of public management platforms. Only by creating more and more docking public management platforms can we promote the in-depth development of public management. This requires local governments to make overall design and arrangement for the public management platform, give full play to the role of public management departments, and further strengthen the unity and docking of the public management platform so that all kinds of data can be jointly constructed and shared. In the process of optimizing the public management platform, local governments also need to build a “public management big data center,” further optimize and improve the public decision-making mechanism, strengthen the overall utilization of data resources, and strengthen the construction of relevant personnel.

The economic growth rate of different districts of Beijing based on the public management platform of Spark big data framework is shown in Figure 6. It can be seen that the ecological environment has little impact on the economic growth rate of each district, which is lower than 10%, while the residents’ income has been greatly improved. The residents’ income of Xicheng District and Chaoyang District is higher than 50%, and the residents’ income of Dongcheng District is the lowest. In addition, urban planning and high happiness index are positively correlated with residents’ income. The higher the residents’ income, the better the urban planning and high happiness index in the area. In general, the public management based on Spark big data framework helps to improve the public management service level of Beijing.

With the increase of urban and rural floating population and the continuous expansion of urban boundary in Beijing, there are problems such as environmental deterioration and traffic congestion in the urban system, which not only interfere with the coordinated operation of the urban system but also create obstacles to its diversified transformation. Due to the promotion and use of Spark big data technology, its intelligent management advantages provide a reference way for the current urban management public services. By reasonably integrating Spark big data technology into the construction of urban management public service system, the efficiency of obtaining information and analyzing problems will be greatly improved, and the management mechanism will also show the characteristics of multichannel, high interaction, and wide coverage. It can further optimize the current operation mode and operation concept, concentrate various resources, and timely and effectively plan urban space, dredge traffic regulatory environment, solve public affairs problems in a more convenient way, improve the effectiveness of urban public service management, and promote the intelligent development of urban system.

The proportion of Beijing public management platforms based on Spark big data framework is shown in Figure 7. It can be seen that the proportion of urban planning is the highest, followed by the happiness index and ecological environment, while the proportion of residents’ income is the lowest. This shows that only by doing a good job in the urban planning of Beijing can we improve the management level of the whole city, which will help to improve the public management service level of Beijing. The urban management system under Spark big data technology can accurately process and detect the basic information of the operation form of the urban system. The high-speed flow of the information network can effectively integrate the information modules of various regions, and the information communication will not be stuck. It can effectively integrate the objects, resources, and other elements of urban management public services in time, unify the corresponding management practices, realize the efficient integration of information and data, improve the openness and integration of information management, and ensure the operation of urban management mechanism more effectively.

5. Conclusion

This paper studies the application innovation of public management based on Spark big data framework, constructs a service platform based on Spark big data framework, takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data, and analyzes its model in detail. Finally, the model based on Spark big data framework is applied to Beijing public management. The experimental results show that the service platform based on Spark big data framework can effectively analyze and predict the public management capacity of Beijing so as to provide strong support for government departments to formulate public management measures.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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Copyright © 2022 Zhi Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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