Methodological Innovation of Public Management Research based on

Big Data and Social Computing

Xingzhou Gao, Tesheng Sun, Mei Zhao and Mengyuan Li

Northwest Normal University, School of Social Development and Public Administration, Lanzhou, China

Keywords: Big Data, Public Management, Data Security, Social Calculation.

Abstract: As the scale and scope of human socio-economic activities have expanded, modern society has become a

complex system with uncertainty and rapid change. The complexity of public problems and the prevalence of

"governance failure" suggest that there is an urgent need to change the thinking of public management

research from that of the industrial society to that of the information society. Through the characteristics of

big data, such as large scale, variety, fast generation and low density, which have great value, big data analysis

and social computing methods are used in combination to build big data analysis models for public

management, to mine effective information and reshape public management research. While big data research

serves public management, it is also important to pay attention to the possible problems of "finiteness".

1 INTRODUCTION

As social and economic prosperity moves forward,

public administration is faced with many new

challenges and opportunities, and the existing public

management methods are often inadequate in dealing

with these complex problems, and the phenomenon

of public management "failure" occurs repeatedly.

The advent of the information age has led to The

Internet of Things, intelligence and big data are

gradually coming into view and becoming an

important support for society to maintain its

operation.

The use of big data thinking has gradually become

an integral part of public management research, and

the practical results it has shown have proven to be a

natural choice in public management practice. Big

data provides a large amount of high-quality

information for public management. By using data

mining and data analysis, public management can

deeply and systematically observe and grasp the

complex behavior of the operation of social systems,

avoiding subjective assumptions and factual

distortions based on statistical data in the traditional

public management process, thus effectively

improving the objectivity, systematization and

relevance of public management. It also helps to

identify new problems in public management, so as

to accurately portray complex public issues, reduce

uncertainty and complexity in the process of public

management, provide auxiliary decisions for public

management decision makers, optimize the level of

public services, and ultimately achieve scientific,

accurate and intelligent public management (Zhang

2013). This is not only an expansion of the public

management system, but also an important innovation

of its way of thinking and behavior.

In today's society of big data and digitalization,

public management should be based on major

theoretical achievements and technological advances,

introduce advanced public management methods,

achieve innovation in research methods, research

objects and research environments, strengthen

multidisciplinary interaction and exchange, and

promote the continuous development of public

management theory and practice.

2 THE DATA SOURCE DILEMMA

OF PUBLIC MANAGEMENT

RESEARCH

Data is a fundamental component in carrying out

public management. In the information age, the full

use of big data thinking and advanced technological

methods to optimize government public decision-

making and improve service levels is an important

part of public management research. In industrialized

Gao, X., Sun, T., Zhao, M. and Li, M.

Methodological Innovation of Public Management Research based on Big Data and Social Computing.

DOI: 10.5220/0011342500003437

In Proceedings of the 1st International Conference on Public Management and Big Data Analysis (PMBDA 2021), pages 263-268

ISBN: 978-989-758-589-0

263

societies, as public issues often exhibit lower levels

of complexity and uncertainty, then methods such as

empirical analysis, statistics and metrics, and logical

analysis can be used to explore and implement public

management laws with universality from individual

structured data and conclusions of limited size and

discontinuity, relying more on personal experience

and statistics for decision making due to limited data

and high acquisition costs.

However, statistics do not accurately reflect the

facts because they often suffer from their endogenous

flaws, such as limited sample size and authenticity (Li

2015). In order to reduce uncertainty and complexity

in public management research, the most effective

way is to expand the sample size and type, obtain big

data with complete information including public

management issues, and control research errors, so

big data naturally becomes an important choice for

public management analysis. In the era of big data,

research in the field of public administration no

longer simply emphasize the accuracy of statistical

data, but rather the search for cause-and-effect

relationships, and quantitative analysis of complex

issues is no longer just sample data, but full-scene

data. For this reason, some scholars consider big data

research as the "fourth paradigm" of research (Tab 1).

Table 1: The four paradigms of scientific discovery.

Scientific

Paradigm

Duration Method

Empirical

More than 1,000

years ago

Description of natural phenomena

Theoretical

Hundreds of years

in the past

Use models, generalizations

Computing

The last few

decades

Simulating complex phenomena

eScience Today

Collecting data using tools, generating data using simulators, processing

data using software, storing data using computers, and analyzing data

At present, the academic community has not yet

formed a complete consensus on the definition of Big

Data, but the different definitions given based on

different perspectives all have their common features,

namely the generalization of the characteristics of Big

Data. Through the elaboration and summary of the

key characteristic attributes of Big Data, a generally

agreed definition of the concept has been formed,

among which the more representative one is "5V"

(Volume, Variety, Velocity, Veracity, Value). In

contrast to the definition of the concept, the key to big

data lies in the technical means to fully acquire and

mine big data, and then explore the value of it through

data analysis, so that it can bring into play social and

economic benefits, and achieve the transformation

from "digital" to "data-based" (Fang 2014). Big data

is the life source of information, and a series of data

collection, transmission, processing and application A

series of technologies related to data collection,

transmission, processing and application constitute

Big Data processing technologies, which are

collectively referred to as "Big Data technologies" for

processing a large amount of structured, semi-

structured and unstructured data generated in the real

world with the help of non-traditional tools in order

to obtain analysis and prediction results. (Fig.1).

Structured

Data

Unstructured Data

Semi-

structured

data

Big data sources Key technologies

Data collection

and processing

Data storage and

management

Computing models

and systems

Data Analysis

and Mining

Data conversion

Data integration

Data cleansing

Distributed File

System

Distributed Data

Data Storage

Batch Calculation

Iterative

calculations

Streaming

Calculation

In-Memory

Computing

Graph Computing

Querying and

Indexing

Semantic Analysis

Human-Computer

Interaction

Visualisation

Big Data Privacy and Security

Other supporting technologies such as data transfer, virtual

clusters

Figure 1: Big Data Technology Road Map.

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3 PUBLIC MANAGEMENT IN

THE PERSPECTIVE OF BIG

DATA

Big Data, as a product of the information society, is a

huge and diverse collection of information that can be

processed to provide strong decision-making aids,

scientific foresight and the ability to optimize

redundant processes, majorly showing the

characteristics of "big", "miscellaneous" and "

correlation" (Cheng 2014). "It is proved that big data

is an effective technical method for value mining and

behavior pre-decision analysis of complex social

systems. Complexity theory shows that even simple

rules in a deterministic system can produce complex

and unpredictable behaviors, and only breakthrough

technologies and methods can be used to face

increasingly complex public management problems.

Data analysis methods, which are commonly used

today, can be very useful in public management

research (Fig. 2).

Big Data Center

Information

interaction control

Internet

Social

COmputing

Visualization

Public

Management

Public Management

Analysis Terminal

Data input

Results of

multidisciplinary

fusion analysis

output

Integrated

system of

methods

Integrated

system Of

experts

Integrated

system of

models

Method base

Information

Base

Models base

Figure 2: Big data analysis model for public management

issues.

3.1 Data Visualization

Data visualization is closely related to information

mapping and information visualization. The main

objective of data visualization is to transform data

information in order to present it in a clear and

graphical way. Generally speaking, charts and maps

can help people to understand information quickly

and accurately based on human cognition, but when

the amount of data to be represented increases to the

level of big data, traditional techniques such as

converting spreadsheets or pictures are no longer able

to handle the huge amount of complex data, and big

data visualization that can assist in algorithm design

and software development has now become an active

research area (Ma 2017).

3.2 Statistical Analysis

Statistical analysis techniques can be briefly divided

into descriptive and inferential statistics. Descriptive

statistical techniques provide a description and

generalization of the overall condition of a data set,

while inferential statistics enable inferences to be

drawn about processes. In addition, more multivariate

statistical analyses include regression, factor analysis,

clustering and discriminant analysis.

3.3 Data Mining

Data mining is an important part of big data

processing and analysis, mainly through specific

algorithms to obtain the key information hidden in a

large amount of data, but also the computational

process of discovering data patterns in big data sets.

At present, many representative and innovative data

mining algorithms have been applied in statistics,

artificial intelligence, machine learning, pattern

recognition, databases, visualisation techniques and

so on. The 10 most influential data mining

algorithms, including C4.5, k-means, SVM, Apriori,

EM, PageRank, AdaBoost, kNN, Parsimonious

Bayes, were summarized at the 2006 ICDM

International Conference and CART, covering the

directions of classification, clustering, regression and

statistical learning (Schönberg 2013). At the same

time, as science and technology continue to advance,

new products such as neural networks and genetic

algorithms are being used to mine data for different

scenarios, and the development of multidisciplinary

cross-fertilisation has led to the fading of boundaries

between many research methods and approaches.

In 2012, KDNuggets conducted a survey of 798

professionals on "Big Data, data mining, and data

analytics software used in real-world projects in the

past year" (A research study 2013). With the help of

big data processing and analysis methods, a variety of

public management objectives can be achieved (Tab

2). The use of big data in the social aspect promotes

the change of social forms, makes social classes flow,

changes the basic norms of social life and the

behavior norms of the social public, forms new social

characters, and becomes a new driving force of social

evolution; the use of big data in the political aspect

changes the traditional political ecology, promotes the

rapid development of network politics and The use of

big data in politics has changed the traditional

Methodological Innovation of Public Management Research based on Big Data and Social Computing

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political ecology, promoted the rapid development of

network politics and network democracy, and led to

the change and transformation of realpolitik; in

economy, it can better carry out economic micro-

macro regulation and control, which is conducive to

the stable and prosperous development of national

economy (Liang 2013).

Table 2: Frequency of use of big data analysis tools.

Big Data Analytics Tools

Software Name Usage frequency

R 30.70%

Excel 29.80%

Rapid-Rapidminer 26.70%

KNMINE 21.80%

Weka/Pentaho 14.80%

4 SOCIAL COMPUTING

THINKING IN PUBLIC

MANAGEMENT

Modern society is a multi-level, interrelated, dynamic

and dissipative complex system, showing the

networked and computable complexity

characteristics of "networking everywhere and

computing everything", and moving from a

"computable society" to a "society of From a

"computable society" to a "digital society" and then

to a "digital society" (Wang 2015). In reality, the most

direct and effective way to address the complexity of

public management is to conduct social experiments.

However, due to resource, ethical, moral,

psychological, cost and risk constraints, social

experimentation is not a viable research method for

public management (Fig. 3).

Statistics

Internet

Monitoring Equipment

Mobile Communication

Sensors

Traffic Information

3S(GIS/GPS/RS)

Satellite Monitoring

Data source

Complex

Systems

Political System

Social System

Economic System

Cultural System

Ecosystem

Public Management

Mains

Content

Technical

Government Society Individual

Public

resources

public

projects

social

issues

Social Sensing/NO-SQL/KNMINE/Weka/R

Figure 3: A social computing model for public management

problems.

Computational thinking is a way of thinking that

uses computer science theory to understand human

social behavior through different levels of

abstraction, to design artificial systems, to solve

complex problems, and to form reliable solutions to

social problems using mathematical models and

methods. It is a way to control complex tasks or

design complex social systems using abstraction and

automatic computation, and it is also a general

understanding and a universal skill for people to

observe, analyze and solve problems in complex

social forms.

In the process of understanding the laws of social

development, human beings have been trying to

answer the philosophical question of whether

"appearance" and "phenomenon" are consistent, that

is, the real relationship between the appearance we

see and the objective phenomenon. For public

management issues, big data is the appearance of

social operation and has distinct social attributes, but

public management big data is not the public issue

itself. Even if we can obtain real and complete big

data of public issues, we cannot achieve real

responses to public issues and reveal the essential

characteristics of public issues without logical

analysis, value analysis and cause-and-effect

analysis. The data itself does not "speak", but the

multi-state public big data needs to be crawled, mined

and analyzed under the guidance of theories of

computational science, statistics, sociology, political

science, philosophy and psychology before it can

"speak" and reveal the true public values and social

attributes hidden in the surface of the data. Therefore,

the analysis requires the presence of the "spiritual

world". At the same time, big data analysis of public

management should also address the conversion of

complex public management from homogeneity to

heterogeneity, and should rely on "spiritual world"

analysis to clarify the social context and discursive

basis of the public actions measured by the data,

otherwise, even the perfect excavation and correct

data inference will only be the product of the

researcher's discursive construction of the public

issues under study. Otherwise, even if the data are

perfectly mined and the inferences are correct, they

will only be the product of the researcher's discourse

on the public issue under study. If the "mental world"

analysis leaves the field, some seemingly "accurate"

big data studies may be deviated from the public issue

phenomenon itself and become worthless from the

very beginning.

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5 SECURITY MANAGEMENT OF

BIG DATA APPLICATIONS

In the field of information technology, security and

privacy have always been critical issues of concern to

all sectors. In the era of big data, all aspects of social

management generate all kinds of data, and with the

dramatic increase and accumulation of large amounts

of data, the data are exposed to serious security risks,

and the traditional data protection methods are no

longer applicable to the rapidly changing social

environment, and the security and privacy of big data

are facing serious challenges (Feng 2014) .At the

same time, the continuous progress of science and

technology, although bringing new security risks and

challenges, also brings significant opportunities for

the field of information security, the development of

science and technology is a double-edged sword,

based on big data generated by information security

technology can also be counterproductive to big data,

to achieve security and privacy protection of big data

(Tab 3).

Table 3: Big Data Security and Key Technologies Related to Privacy Protection.

Key technologies for big data security

Big Data Security and Privacy Protection Technology Big data service and information security technology

Data release anonymity protection technology

Big Data-based threat discovery technology

(IBM Big Data Security Intelligence)

Social network anonymity protection technology Big Data-based authentication technology

Data watermarking technology Big Data-based data authenticity analysis

Data Provenance Technology Big Data and Security-as-a-Service

Role mining

Risk-adaptive access control

5.1 Privacy of Big Data

In the era of big data, the privacy issue of data

includes two aspects: on the one hand, the protection

of personal privacy, with the development of data

collection technology, in the user is not aware of,

personal interests, habits, physical characteristics and

other private information can be more easily

accessed; on the other hand, even with the permission

of the user, personal privacy data in the process of

storage, transmission and use, there is a risk of being

leaked. The analytic power of big data leads to the

possibility that seemingly simple information can be

mined for privacy, so privacy protection in the face of

big data era will become a new proposition.

5.2 Data Quality

Data quality affects the utilization of big data, and low

quality data not only wastes transmission and storage

resources, but also cannot be used. There are many

factors that affect the quality of data, which can be

affected in the process of generation, acquisition,

transmission and storage. Data quality is expressed in

terms of accuracy, completeness, redundancy, and

consistency. Although there are many measures to

improve data quality, the problem of data quality

cannot be completely eradicated. Therefore, there is a

need to investigate a method that can automatically

detect data quality and can repair some of the data

with quality problems by itself.

5.3 Big Data Security Mechanisms

Big data brings challenges to data encryption in terms

of data size and data variety. Previous encryption

methods for small and medium scale cannot meet the

requirements of big data in terms of performance, and

efficient big data cryptography needs to be studied.

For structured, semi-structured and unstructured data

with different structures, there is a need to study how

to effectively perform security management, access

control and secure communication. In addition, in the

multi-tenant model, it is necessary to achieve the

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isolation, confidentiality, integrity, availability,

controllability and traceability of tenant data while

ensuring efficiency.

5.4 Big Data Applications in the Field

of Information Security

Big data not only brings challenges to information

security, but also injects new momentum into the

development of information security. For example,

through big data analysis of log files of intrusion

detection systems, potential security vulnerabilities

and advanced sustainability threats (APT) can be

identified. In addition, information such as virus

characteristics, vulnerability characteristics and

attack characteristics can be more easily grasped

through big data analysis. In summary, the security

issue of big data has gained much attention from

domestic and foreign researchers, however, the

current research on the representation, metrics and

semantic understanding methods of multi-source

heterogeneous big data, modeling theory and

computation.

6 CONCLUSION

In the era of big data, the collection, acquisition and

analysis of data are faster, and these massive data will

have a profound impact on human society. The

application of big data to public management process

is to explore the potential value from big data through

the method of data analysis, and according to the

different ways of data generation and structural

characteristics, it can act in different areas of public

management. It is worth noting that the structural

complexity and meaningful complexity of big data

brings the problem of complexity in social

computing. No matter how much data is used,

predictions inevitably encounter subjective value

judgments and cannot be truly accurate, making the

effect of big data analysis limited. At the same time,

the connotation, technology and methods of big data

application to public management are still immature,

and will face a variety of problems and technical

challenges in the process of its development. The

technologies of efficient data storage, effective data

acquisition, data analysis, data presentation and data

security in big data analysis are yet to be further

developed.

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