scholarly journals The application framework of big data technology in the COVID-19 epidemic emergency management in local government—a case study of Hainan Province, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zijun Mao ◽  
Qi Zou ◽  
Hong Yao ◽  
Jingyi Wu
Keyword(s):  
Big Data ◽  
Emergency Management ◽  
Local Governments ◽  
Management Strategies ◽  
Application Framework ◽  
Hainan Province ◽  
Technology Application ◽  
Management Measures ◽  
Big Data Technology

Abstract Background As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China’s SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. Methods This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan’s application of big data technology in its COVID-19 epidemic emergency management. Results Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. Conclusions This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

scholarly journals The Strategy of Salt Business Development: A Case Study in Sumenep, Indonesia

2021 ◽  
Vol 226 ◽  
pp. 00036
Author(s):  
Rika Diananing ◽  
Amilia Destryana ◽  
Ribut Santosa ◽  
Noor Illi Mohamad Puad ◽  
Agustine Christela Melviana
Keyword(s):  
Development Strategy ◽  
Business Strategy ◽  
Swot Analysis ◽  
Management Strategies ◽  
Business Management ◽  
Production Method ◽  
Technology Application ◽  
Cooperation Strategy

Sumenep is one of the salt producers in Indonesia. The problem experienced by farmers is the production of salt using evaporation by solar energy that depends on the weather and the low price, caused by worse business management. Salt is a potential commodity, because its market is still wide open. This research aims to develop the salt production method and development strategy of salt business in Sumenep Regency by using SWOT analysis. The result of the analysis concludes that the priority of salt development business strategy in Sumenep Regency are: i) Geoisolator technology application strategy to produce good quality of salt; ii) cooperation strategy in group mechanism to build power and increasing the bargaining value of the farmers; iii) capital strengthening strategy through partner cooperatives; iv) broader marketing management management strategies to industrial salt user sectors.

A Social Media Big Data-Based Disaster Assessment Framework for Typhoon-induced Flood: Case Study of Typhoon Lekima

2020 ◽  
Author(s):  
Jiting Tang ◽  
Saini Yang ◽  
Weiping Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Emergency Management ◽  
Text Classification ◽  
Text Clustering ◽  
Assessment Framework ◽  
The Public ◽  
Source Data ◽  
Disaster Assessment

<p>In 2019, the typhoon Lekima hit China, bringing strong winds and heavy rainfall to the nine provinces and municipalities on the northeastern coast of China. According to the Ministry of Emergency Management of the People’s Republic of China, Lekima caused 66 direct fatalities, 14 million affected people and is responsible for a direct economic loss in excess of 50 billion yuan. The current observation technologies include remote sensing and meteorological observation. But they have a long time cycle of data collection and a low interaction with disaster victims. Social media big data is a new data source for natural disaster research, which can provide technical reference for natural hazard analysis, risk assessment and emergency rescue information management.</p><p>We propose an assessment framework of social media data-based typhoon-induced flood assessment, which includes five parts: (1) <strong>Data acquisition.</strong> Obtain Sina Weibo text and some tag attributes based on keywords, time and location. (2) <strong>Spatiotemporal quantitative analysis.</strong> Collect the public concerns and trends from the perspective of words, time and space of different scales to judge the impact range of typhoon-induced flood. (3) <strong>Text classification and multi-source heterogeneous data fusion analysis.</strong> Build a hazard intensity and disaster text classification model by CNN (Convolutional Neural Networks), then integrate multi-source data including meteorological monitoring, population economy and disaster report for secondary evaluation and correction. (4) <strong>Text clustering and sub event mining.</strong> Extract subevents by BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) text clustering algorithms for automatic recognition of emergencies. (5) <strong>Emotional analysis and crisis management.</strong> Use time-space sequence model and four-quadrant analysis method to track the public negative emotions and find the potential crisis for emergency management.</p><p>This framework is validated with the case study of typhoon Lekima. The results show that social media big data makes up for the gap of data efficiency and spatial coverage. Our framework can assess the influence coverage, hazard intensity, disaster information and emergency needs, and it can reverse the disaster propagation process based on the spatiotemporal sequence. The assessment results after the secondary correction of multi-source data can be used in the actual system.</p><p>The proposed framework can be applied on a wide spatial scope and even full coverage; it is spatially efficient and can obtain feedback from affected areas and people almost immediately at the same time as a disaster occurs. Hence, it has a promising potential in large-scale and real-time disaster assessment.</p>

scholarly journals Construction of Ecological Environment Information System Based on Big Data: A Case Study on Dongting Lake Ecological Area

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandi Wan ◽  
Shaohua Yin
Keyword(s):  
Information System ◽  
Big Data ◽  
Rapid Development ◽  
Dongting Lake ◽  
Ecological Environment ◽  
Ecological Problem ◽  
Collection Data ◽  
Technology Trend ◽  
Big Data Technology

With the rapid development of cloud computing, Internet of Things, and other technologies, the information technology trend led by “big data” has an impact on all fields. The application of big data technology in the field of ecological environmental protection enables accurate and comprehensive ecological information collection, data analysis, and mining, accurate ecological problem identification, and effective solution. Taking Dongting Lake Ecological Area as an example, this paper constructs an ecological environment information system based on big data and expounds its specific application in water, atmosphere, soil environment monitoring, and pollution control, aiming to provide a reference for the application of big data technology in the field of ecological environment protection in Dongting Lake Ecological Area and more effectively maintain the ecological environmental quality and safety in the area.

Challenges in managing invasive raccoons in Japan

2019 ◽  
Vol 46 (6) ◽  
pp. 476
Author(s):  
Takaaki Suzuki ◽  
Tohru Ikeda
Keyword(s):  
Local Governments ◽  
Best Practice ◽  
Management Strategies ◽  
Monitoring Program ◽  
Relevant Information ◽  
Municipal Governments ◽  
Management Regime ◽  
Management Measures ◽  
Invasion Stage ◽  
Management Procedures

Context In Japan, the raccoon is an invasive, non-native mammal that causes significant agricultural damage and impacts on native biodiversity throughout the country. Local governments are mainly responsible for raccoon management. Intensive control campaigns focused on the early invasion stage have controlled raccoons in some regions but, generally, there are very few regions where raccoon numbers have been reduced sustainably, and no raccoon populations have been eradicated. Aims To improve national management of raccoons and canvass the opinions and perceptions of local government officers involved in raccoon control, and to review the efficiency and effectiveness of raccoon management strategies. Methods A questionnaire survey of 47 prefectural and 366 municipal governments was conducted, regarding raccoon management measures, during 2012 and 2013. The survey covered two topics: (1) management difficulties experienced by officers; and (2) details of the current raccoon management regime. Key results Efforts to manage raccoon populations have encountered some difficulties, including shortages of raccoon control officers, funding, expertise in raccoon biology and management, and lack of information about the invasion status of local raccoon populations and ecological traits of raccoons. Prefectures not currently managing raccoons indicated that they suffered from a lack of appropriate management procedures. However, current management programs were not generally functioning efficiently or effectively because many local governments did not implement appropriate monitoring. About 70% of local governments did not set control target indices, and there were very few quantitative datasets that could be used to measure the effectiveness of control in reducing raccoon impacts. Conclusions Best practice management programs have been being implemented in very few government areas, with institutional characteristics and difficulties in obtaining relevant information causing major problems. Implications Collecting and sharing information about effective raccoon management methods and case study examples from successful regions would enable other local administrations to select and implement the most effective and efficient control strategy, methods and monitoring program for their region.

scholarly journals Practical Recommendation for the Use of Big Data in Strategic Management: Case Study of a Kazakh Telecommunications Company

2018 ◽  
Vol 9 (3) ◽  
pp. 88
Author(s):  
Gulnara Z. Karimova ◽  
Yevgeniya Kim
Keyword(s):  
Big Data ◽  
Strategic Management ◽  
Economic Benefits ◽  
Practical Recommendation ◽  
Tactical Decisions ◽  
Management Case Study ◽  
Decision Making Tool ◽  
Big Data Technology ◽  
Telecommunications Company

This study analyzes the dynamics in the development and use of new, innovative technologies based on big data as they allow companies to expand their range of services, using large amounts of data, which in turn enables them to obtain economic benefits. Big data is often used as a tool for undertaking managerial tactical decisions rather than strategic. Using the practices of a Kazakh telecommunications company as an example, this study demonstrates how the potential of big data is limited to the decision-making tool and suggests how big data technology can improve the efficiency of the strategic management.

scholarly journals Analysis on the influence of big data era on enterprise financial accounting -- a case study of HUA WEI technologies co., LTD

2020 ◽  
Vol 214 ◽  
pp. 01004
Author(s):  
Wang Yang
Keyword(s):  
Big Data ◽  
Financial Accounting ◽  
Work Flow ◽  
Electronic Document ◽  
Application Process ◽  
Financial Decision Making ◽  
Financial Decision ◽  
The Impact ◽  
Big Data Technology

”Big data” is the product of the integration of the highly developed Internet innovation function and various economic fields in today’s society. The development of “big data” is bound to bring significant changes in the economic development of today’s society. Taking HUA WEI technologies co., LTD., financial aspects based on the development of big data, found big data technology in the application process of the impact of the financial accounting, this era of big data work flow for the company in China, the impact of financial decision-making and financial personnel, and the company response to this phenomenon and make a change, and to analyze its causes and solutions. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

scholarly journals Digital contact tracing for COVID-19 epidemic emergency management—A case study based on graph database algorithm (Preprint)

2020 ◽  
Author(s):  
Zijun Mao ◽  
Hong Yao ◽  
Qi Zou ◽  
Weiting Zhang ◽  
Ying Dong
Keyword(s):  
Big Data ◽  
High Risk ◽  
Emergency Management ◽  
Coordination Mechanism ◽  
Contact Tracing ◽  
Hainan Province ◽  
Graph Database ◽  
Public Places ◽  
Data Platform ◽  
Centralized Model

BACKGROUND The Coronavirus Disease 2019 (COVID-19) epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management, but traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local government to trace the contacts of COVID-19 cases more comprehensively, efficiently, and precisely. OBJECTIVE Hainan Province, China was selected in this case study for the introduction of a new digital contact tracing method under the centralized model, that is, using graph database algorithm, to analyze multi-source COVID-19 epidemic data to achieve contact tracing on the government’s big data platform. Our research hoped to provide new solutions to break through the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of the digital contact tracing in Hainan Province. METHODS Graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province, which relies on the government’s big data platform, to analyze multi-source COVID-19 epidemic data and build networks of the relationship among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. RESULTS An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multi-source epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible and can analyze multi-source and heterogeneous epidemic big data. These practices quickly and accurately identified and traced 10,871 contacts among hundreds of thousands of epidemic data records and identified 378 most-close contacts and a batch of high-risk infected public places. A confirmed patient was found after quarantine measures were implemented on all contacts. CONCLUSIONS An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multi-source epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible and can analyze multi-source and heterogeneous epidemic big data. These practices quickly and accurately identified and traced 10,871 contacts among hundreds of thousands of epidemic data records and identified 378 most-close contacts and a batch of high-risk infected public places. A confirmed patient was found after quarantine measures were implemented on all contacts.

A Semantic++ MapReduce Parallel Programming Model

2014 ◽  
Vol 08 (03) ◽  
pp. 279-299
Author(s):  
Guigang Zhang ◽  
Chao Li ◽  
Yong Zhang ◽  
Chunxiao Xing
Keyword(s):  
Big Data ◽  
Parallel Programming ◽  
Data Centers ◽  
Programming Model ◽  
Application Framework ◽  
Parallel Programming Model ◽  
Internet Finance ◽  
Real Time Computing ◽  
Huge Challenge

Big data is playing a more and more important role in every area such as medical health, internet finance, culture and education etc. How to process these big data efficiently is a huge challenge. MapReduce is a good parallel programming language to process big data. However, it has lots of shortcomings. For example, it cannot process complex computing. It cannot suit real-time computing. In order to overcome these shortcomings of MapReduce and its variants, in this paper, we propose a Semantic++ MapReduce parallel programming model. This study includes the following parts. (1) Semantic++ MapReduce parallel programming model. It includes physical framework of semantic++ MapReduce parallel programming model and logic framework of semantic++ MapReduce parallel programming model; (2) Semantic++ extraction and management method for big data; (3) Semantic++ MapReduce parallel programming computing framework. It includes semantic++ map, semantic++ reduce and semantic++ shuffle; (4) Semantic++ MapReduce for multi-data centers. It includes basic framework of semantic++ MapReduce for multi-data centers and semantic++ MapReduce application framework for multi-data centers; (5) A Case Study of semantic++ MapReduce across multi-data centers.

Sign in / Sign up

Export Citation Format

Share Document