The big data analysis and mining of people’s livelihood appeal based on time series modeling and algorithm

The big data analysis and mining of people's livelihood appeal based on time series modeling and algorithm

Table of Contents





Abstract

In order to analyze the big data of people’s livelihood appeal, this paper proposes a time series modeling and algorithm to decompose the time series {x(t)} of data into long-term change trend L(t), the short-term change trend S(t) and occasional change e(t). Then use this method to break down the data of six types of people’s livelihood appeal such as unlicensed vendor, industrial noise, sewer cover, academic qualification, out-of-store operation and public transportation, combine other data for correlation analysis, find out the cause of the appealing event and make predictions. The experimental results verify the effectiveness of time series analysis in big data analysis and mining of people’s livelihood appeal, and it is an useful attempt in the analysis of e-government big data.

 

  • Author Keywords

    • Time series analysis,
    • Big data analysis and mining,
    • People’s livelihood appeal

 

Introduction

In recent years, with the rapid growth of the amount of data in various fields, we urgently need to transform data into information and knowledge. Time series analysis is a method that uses statistical techniques to mathematically model a series of numbers based on chronological order and discover the pattern of data changes [1]. Time series analysis has a wide range of applications such as: stock analysis and forecasting [2] [3], business sales analysis and forecasting [4] [5], signal analysis [6], traffic flow analysis [7], environmental monitoring [8], detection of abnormal network traffic [9], and disease prediction [10], etc. At present, the application of time series analysis in big data analysis and mining of government affairs is relatively few, which is a blank area that deserves attention [11]. At the information age, traditional government management has been unable to meet the needs of new demand [12]. More and more countries in the world are committed to formulating a “government data disclosure” strategy, which is designed to inspire people to re-use or innovatively process public data to derive new models and create value; to help public sector personnel to make better decisions and improve efficiency [13]. Big data analysis and mining based on time series analysis in e-government management is of great significance.

Aiming at the field of people’s livelihood appeal in government affairs, this paper proposes big data analysis and mining using time series modeling and algorithm. People’s livelihood appeal refers to the opinions and needs of people involving all aspects of society. It is a way for the government to interact with people. Many local governments pay attention to the optimization and reform of appeal processes, integrate multiple appeal channels and establish a standard platform to uniformly manage the big data of various livelihood appeals. In response to current needs, this paper uses time series modeling and algorithm to deeply analyze big data of people’s livelihood appeal from multiple dimensions, and provide support for the service-oriented government to make decisions. Section 2 of this paper introduces time series analysis modeling and algorithm of people’s livelihood appeal. Section 3 analyzes the experimental data of people’s livelihood appeal in detail and shows the results. Finally, Section 4 summarizes the research work of this paper.

Conclusion

Time series analysis is a method that uses statistical techniques to discover the pattern of data changes and has important significance in our life. At present, the application of time series techniques in big data analysis and mining of government affairs is relatively few. In this paper, 39,788 people ’s livelihood appeals reported by the public via WeChat, email, hotline, Weibo, and other channels on government open platform were used to analyze the time-series data by additive combination model. The change of time series is decomposed into three sub-categories of long-term change trend, short-term change trend, and occasional change. Big data analysis and mining are performed on the data set using the time series analysis method. Through experiments and analysis, we have discovered the changing patterns of people ’s livelihood appeal, analyzed the causes of the events, and made predictions. The effectiveness of the time series analysis method was verified, which could help the government to conduct research, make decisions, and solve people’s livelihood issues. Future research work will be carried out in the following areas: 1) Conduct more analysis on the relations of data, like appeal location, appeal group, or individual; 2) Apply a time-series outlier detection approach for occasional changes.

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The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

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FULL Paper PDF file:

The big data analysis and mining of people's livelihood appeal based on time series modeling and algorithm

Bibliography

author

L. Lixin and L. Lin,

Year

2020

Title

The big data analysis and mining of people’s livelihood appeal based on time series modeling and algorithm

Publish in

2020 International Conference on High-Performance Big Data and Intelligent Systems (HPBD&IS), Shenzhen, China, 2020, pp. 1-5,

Doi

10.1109/HPBDIS49115.2020.9130588.

PDF reference and original file: Click here

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.