Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline...By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.展开更多
This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can e...This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can enhance the efficiency of bank data processing,enrich data types,and strengthen data analysis and application capabilities.In response to future development needs,it is necessary to strengthen data collection management,enhance data processing capabilities,innovate big data application models,and provide references for bank big data practices,promoting the transformation and upgrading of the banking industry in the context of legal digital currencies.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This...With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.展开更多
A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data prov...A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.展开更多
The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibi...The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.展开更多
Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modula...Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.展开更多
As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user sat...As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and anger.Subsequently,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements.The results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative influence.However,the influence of expectation confirmation on user satisfaction is not supported.Moreover,sadness,anxiety,and anger are positively related to the perceived risk of users.Compared with sadness and anxiety,anger has a more important role in increasing the perceived burden of users.Based on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services.The proposed big data approach may be utilized in other UX studies in the future.展开更多
Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by...Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.展开更多
Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data vis...Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.展开更多
The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, custome...The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.展开更多
In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emoti...In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.展开更多
In the era of big data, huge volumes of data are generated from online social networks, sensor networks, mobile devices, and organizations’ enterprise systems. This phenomenon provides organizations with unprecedente...In the era of big data, huge volumes of data are generated from online social networks, sensor networks, mobile devices, and organizations’ enterprise systems. This phenomenon provides organizations with unprecedented opportunities to tap into big data to mine valuable business intelligence. However, traditional business analytics methods may not be able to cope with the flood of big data. The main contribution of this paper is the illustration of the development of a novel big data stream analytics framework named BDSASA that leverages a probabilistic language model to analyze the consumer sentiments embedded in hundreds of millions of online consumer reviews. In particular, an inference model is embedded into the classical language modeling framework to enhance the prediction of consumer sentiments. The practical implication of our research work is that organizations can apply our big data stream analytics framework to analyze consumers’ product preferences, and hence develop more effective marketing and production strategies.展开更多
Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with o...Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.展开更多
This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South K...This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.展开更多
Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university...Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university’s big data.Taking the student card information as the research sample,using spark big data mining technology and K-Means clustering algorithm,taking scholarship evaluation as an example,the big data is analyzed.Data includes analysis of students’daily behavior from multiple dimensions,and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism,votes of teachers and students,etc.At the same time,students’absenteeism,physical health and psychological status in advance can be predicted,which makes student management work more active,accurate and effective.展开更多
There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and co...There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and computer science. Big data analysis is an effective way to build scientific hypothesis and explore internal mechanism.Here, gene expression is taken as an example to illustrate the basic procedure of the big data analysis.展开更多
The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on pr...The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.展开更多
With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control ...With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.展开更多
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
文摘By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.
文摘This paper analyzes the advantages of legal digital currencies and explores their impact on bank big data practices.By combining bank big data collection and processing,it clarifies that legal digital currencies can enhance the efficiency of bank data processing,enrich data types,and strengthen data analysis and application capabilities.In response to future development needs,it is necessary to strengthen data collection management,enhance data processing capabilities,innovate big data application models,and provide references for bank big data practices,promoting the transformation and upgrading of the banking industry in the context of legal digital currencies.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
文摘With the advent of the big data era,real-time data analysis and decision-support systems have been recognized as essential tools for enhancing enterprise competitiveness and optimizing the decision-making process.This study aims to explore the development strategies of real-time data analysis and decision-support systems,and analyze their application status and future development trends in various industries.The article first reviews the basic concepts and importance of real-time data analysis and decision-support systems,and then discusses in detail the key technical aspects such as system architecture,data collection and processing,analysis methods,and visualization techniques.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2018R1D1A1B07040282 to JJ)+1 种基金a grant from Kyung Hee University in 2018(KHU-20181065 to JJ)
文摘A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system.This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression(fold change) but they are weakly expressed in experimental studies(based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies;such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016(up-and downregulated) genes were determined to be related to Schwann cells. However,no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes.Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.
文摘The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.
基金National Key R&D Program of China(Grant No.2018YFB1701701)Sailing Talent Program+1 种基金Guangdong Provincial Science and Technologies Program of China(Grant No.2017B090922008)Special Grand Grant from Tianjin City Government of China。
文摘Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.
基金supported by a National Research Foundation of Korea(NRF)(http://nrf.re.kr/eng/index)grant funded by the Korean government(NRF-2020R1A2C1014957).
文摘As of 2020,the issue of user satisfaction has generated a significant amount of interest.Therefore,we employ a big data approach for exploring user satisfaction among Uber users.We develop a research model of user satisfaction by expanding the list of user experience(UX)elements(i.e.,pragmatic,expectation confirmation,hedonic,and burden)by including more elements,namely:risk,cost,promotion,anxiety,sadness,and anger.Subsequently,we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements.The results of a regression analysis reveal the following:hedonic,promotion,and pragmatic significantly and positively affect user satisfaction,while burden,cost,and risk have a substantial negative influence.However,the influence of expectation confirmation on user satisfaction is not supported.Moreover,sadness,anxiety,and anger are positively related to the perceived risk of users.Compared with sadness and anxiety,anger has a more important role in increasing the perceived burden of users.Based on these findings,we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services.The proposed big data approach may be utilized in other UX studies in the future.
基金We are grateful to the support from the National Natural Science Foundation of China （NSFC） （31425002, 91231205, 81430011, 61303161, 31470220, and 31327001）, and the Frontier Science Research Program, the Soil-Microbe System Function and Regulation Program, and the Science and Technology Service Network Initiative （STS） from the Chinese Academy of Sciences （CAS）.
文摘Method development has always been and will continue to be a core driving force of microbiome science, In this perspective, we argue that in the next decade, method development in microbiome analysis will be driven by three key changes in both ways of thinking and technological platforms： ① a shift from dissecting microbiota structure by sequencing to tracking microbiota state, function, and intercellular interaction via imaging; ② a shift from interrogating a consortium or population of cells to probing individual cells; and ③a shift from microbiome data analysis to microbiome data science. Some of the recent methoddevelopment efforts by Chinese microbiome scientists and their international collaborators that underlie these technological trends are highlighted here. It is our belief that the China Microbiome Initiative has the opportunity to deliver outstanding ＂Made-in-China＂ tools to the international research community, by building an ambitious, competitive, and collaborative program at the forefront of method development for microbiome science.
基金This research work is supported by Hunan Provincial Education Science 13th Five Year Plan(Grant No.XJK016BXX001)Social Science Foundation of Hunan Province(Grant No.17YBA049)+2 种基金Hunan Provincial Natural Science Foundation of China(Grant No.2017JJ2016)National Students’platform for innovation and entrepreneurship training(Grant No.201811532010)The work is also supported by Open foundation for University Innovation Platform from Hunan Province,China(Grand No.16K013)and the 2011 Collaborative Innovation Center of Big Data for Financial and Economical Asset Development and Utility in Universities of Hunan Province.We also thank the anonymous reviewers for their valuable comments and insightful suggestions.
文摘Graphical methods are used for construction.Data analysis and visualization are an important area of applications of big data.At the same time,visual analysis is also an important method for big data analysis.Data visualization refers to data that is presented in a visual form,such as a chart or map,to help people understand the meaning of the data.Data visualization helps people extract meaning from data quickly and easily.Visualization can be used to fully demonstrate the patterns,trends,and dependencies of your data,which can be found in other displays.Big data visualization analysis combines the advantages of computers,which can be static or interactive,interactive analysis methods and interactive technologies,which can directly help people and effectively understand the information behind big data.It is indispensable in the era of big data visualization,and it can be very intuitive if used properly.Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships,and it represents a significant business opportunity.With the rise of big data,important technologies suitable for dealing with complex relationships have emerged.Graphics come in a variety of shapes and sizes for a variety of business problems.Graphic analysis is first in the visualization.The step is to get the right data and answer the goal.In short,to choose the right method,you must understand each relative strengths and weaknesses and understand the data.Key steps to get data:target;collect;clean;connect.
文摘The proliferation of textual data in society currently is overwhelming, in particular, unstructured textual data is being constantly generated via call centre logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, etc.While the amount of textual data is increasing rapidly, users ability to summarise, understand, and make sense of such data for making better business/living decisions remains challenging. This paper studies how to analyse textual data, based on layered software patterns, for extracting insightful user intelligence from a large collection of documents and for using such information to improve user operations and performance.
文摘In this paper,we describe a method of emotion analysis on social big data.Social big data means text data that is emerging on Internet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis.Because these data are constructed by manual annotation,their quality is high but their quantity is low.If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data,we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications,which we could not know before.In this paper,we create an emotion analysis model that integrate the highquality emotion corpus and the automaticconstructed corpus that we created in our past studies,and then analyze a large-scale corpus consisting of Twitter tweets based on the model.As the result of time-series analysis on the large-scale corpus and the result of model evaluation,we show the effectiveness of our proposed method.
文摘In the era of big data, huge volumes of data are generated from online social networks, sensor networks, mobile devices, and organizations’ enterprise systems. This phenomenon provides organizations with unprecedented opportunities to tap into big data to mine valuable business intelligence. However, traditional business analytics methods may not be able to cope with the flood of big data. The main contribution of this paper is the illustration of the development of a novel big data stream analytics framework named BDSASA that leverages a probabilistic language model to analyze the consumer sentiments embedded in hundreds of millions of online consumer reviews. In particular, an inference model is embedded into the classical language modeling framework to enhance the prediction of consumer sentiments. The practical implication of our research work is that organizations can apply our big data stream analytics framework to analyze consumers’ product preferences, and hence develop more effective marketing and production strategies.
文摘Opinion (sentiment) analysis on big data streams from the constantly generated text streams on social media networks to hundreds of millions of online consumer reviews provides many organizations in every field with opportunities to discover valuable intelligence from the massive user generated text streams. However, the traditional content analysis frameworks are inefficient to handle the unprecedentedly big volume of unstructured text streams and the complexity of text analysis tasks for the real time opinion analysis on the big data streams. In this paper, we propose a parallel real time sentiment analysis system: Social Media Data Stream Sentiment Analysis Service (SMDSSAS) that performs multiple phases of sentiment analysis of social media text streams effectively in real time with two fully analytic opinion mining models to combat the scale of text data streams and the complexity of sentiment analysis processing on unstructured text streams. We propose two aspect based opinion mining models: Deterministic and Probabilistic sentiment models for a real time sentiment analysis on the user given topic related data streams. Experiments on the social media Twitter stream traffic captured during the pre-election weeks of the 2016 Presidential election for real-time analysis of public opinions toward two presidential candidates showed that the proposed system was able to predict correctly Donald Trump as the winner of the 2016 Presidential election. The cross validation results showed that the proposed sentiment models with the real-time streaming components in our proposed framework delivered effectively the analysis of the opinions on two presidential candidates with average 81% accuracy for the Deterministic model and 80% for the Probabilistic model, which are 1% - 22% improvements from the results of the existing literature.
基金This research was supported by a grant[KCG-01-2017-01]through the Disaster and Safety Management Institute funded by the Ministry of Public Safety and Securitythe National Research Foundation of Korea(NRF)grant[No.2018R1D1A1B07050208]funded by the Ministry of Science and ICT of Korea Government.
文摘This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.
基金Nanjing Key Laboratory of Intelligent Information Processing Open Fund Project(No.19AIP05)。
文摘Big data analysis has penetrated into all fields of society and has brought about profound changes.However,there is relatively little research on big data supporting student management regarding college and university’s big data.Taking the student card information as the research sample,using spark big data mining technology and K-Means clustering algorithm,taking scholarship evaluation as an example,the big data is analyzed.Data includes analysis of students’daily behavior from multiple dimensions,and it can prevent the unreasonable scholarship evaluation caused by unfair factors such as plagiarism,votes of teachers and students,etc.At the same time,students’absenteeism,physical health and psychological status in advance can be predicted,which makes student management work more active,accurate and effective.
文摘There are a lot of biological and experimental data from genomics, proteomics, drug screening, medicinal chemistry, etc. A large amount of data must be analyzed by special methods of statistics, bioinformatics, and computer science. Big data analysis is an effective way to build scientific hypothesis and explore internal mechanism.Here, gene expression is taken as an example to illustrate the basic procedure of the big data analysis.
基金We thank the anonymous reviewers and editors for their very constructive comments.the National Social Science Foundation Project of China under Grant 16BTQ085.
文摘The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications.The existing researches on user privacy protection in mobile social network mainly focus on privacy preserving data publishing and access control.There is little research on the association of user privacy information,so it is not easy to design personalized privacy protection strategy,but also increase the complexity of user privacy settings.Therefore,this paper concentrates on the association of user privacy information taking big data analysis tools,so as to provide data support for personalized privacy protection strategy design.
基金Key Major of Audit Science in quality Engineering Project of Private Universities in 2020(Grant No.:HS2020ZLGC06)Supervisor System Research Project of Huashang College of Guangdong University of Finance and Economics in 2018(Grant No.:2018HSDS03)University Quality Engineering of Huashang College in 2021(Grant No.:HS2021ZLGC19)。
文摘With the arrival of the era of big data,the audit thinking mode has been promoted to change.Under the influence of big data,audit will become an activity of continuous behavio Through cloud data,the staff can control the operation status and risk assessment of the whole enterprise,timely analyze,control and respond to risks,and protect the enterprise to reduce risks.With the advent of the era of big data,audit data analysis is becoming more and more important.At the same time,a large amount of data analysis also brings challenges to auditors.Methods to deal and solve the challenges has become an urgent problem to be solved at present.This paper mainly studies the challenges and countermeasures brought by the changes of audit approaches and methods to audit data analysis under the background of big data,so as to continuously innovate and practice the improvement of audit technology and promote the healthy and rapid development of social economy.
