Big Data Resources Research Articles

BP Neural Network-Based Big Data Intelligent Travel Algorithm and Its Application

With the increase of urbanization rate, a large number of people flood into cities, increasing pressure of urban traffic, and problems accumulated in the taxi industry are gradually prominent. The phenomenon of crowded queue for taxi is frequent in peak hours, and vehicles patrol and sweep streets during peak hours. The key to solve these problems lies in mastering the rules and patterns of taxi travel and finding the factors affecting the relationship between taxi supply and demand. It is difficult to effectively understand taxi travel as a whole due to the large number of taxis and their large scale and strong mobility. Comprehensive application of trajectory data mining method can extract the spatiotemporal characteristics of massive taxi trajectory data and reveal the nature of its occurrence. This paper mainly focuses on the problems faced by urban traffic governance, such as the mismatch between data sources and demand systems, the uncoordinated operation of comprehensive transportation system, and the difficulty in sharing big data resources between government and enterprises. With massive data resources across departments, this paper designed a networked intelligent computing platform for big data of urban transportation integrated with various modes of transportation to sense the operation situation of urban comprehensive transportation system in real time, accurately grasp the space-time distribution of urban transportation supply and demand, and significantly improve the ability of coordinated operation, organization, and management of transportation in large cities. It also effectively enhances the quality of transportation information sharing and integration services and comprehensively improves the efficiency and overall carrying capacity of the urban comprehensive transportation system.

Non-sampling errors in questionnaire surveys: findings from a National Fertility Survey

Non-sampling errors can generally be divided into three types: sampling frame errors, non-response errors and measurement errors. Missing target units in the sampling frame, improper handling of non-responses, and misreporting or underreporting of key variables in the questionnaire can all cause deviations in a survey’s results. The widespread application of Computer-Assisted Personal Interviewing (CAPI) systems and the inclusion of administrative records from government sources in surveys has strengthened the ability to control non-sampling errors. Taking a national fertility sampling survey as an example, this study summarizes the sources of various non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey. The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors. The findings indicate that non-sampling errors were the main source of total error in the survey, and that the errors found came mainly from sampling frame errors; non-response errors and measurement errors were controlled and had little impact on the survey results.

The Impact of Big Data Technical Skills on Novel Business Model Innovation Based on the Role of Resource Integration and Environmental Uncertainty

Taking the dynamic capability theory as the research framework, using a two-stage questionnaire survey and 318 Chinese enterprises as samples, this study investigates the impact mechanism of big data technical skills on novel business model innovation, as well as the mediator of resource integration and the moderator of environmental uncertainty. The hierarchical regression results show that: (1) big data technical skills have a significant positive impact on novel business model innovation; (2) resource integration is a partial mediator between big data technical skills and novel business model innovation; (3) environmental uncertainty regulates the relationship between big data technical skills and resource integration, that is, the higher the environmental uncertainty, the stronger the positive relationship between big data technical skills and resource integration; (4) environmental uncertainty has no significant moderating effect on the mediating role of resource integration between big data technology skills and novel business model innovation. The conclusion of this study develops the driving factors of novel business model innovation and provides theoretical guidance for enterprises to improve the innovative activities of novel business models by using big data technology skills.

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

Estimates of methane and nitrous oxide emission from a rice field in Central Java, Indonesia, based on the DeNitrification DeComposition model

<p>Indonesia is the world’s third largest rice producer, with most rice being cultivated (estimated 3.1 million ha) in Central Java. However, one of the environmental challenges in producing rice is greenhouse gas (GHG) emissions from rice fields. Therefore, understanding the GHG emissions (methane and nitrous oxide) from the rice farming system is important for better management practices. The objective of this study is to estimate the GHG emissions supported by a satellite database, namely, the DeNitrification DeComposition (DNDC) model, at three regencies at Central Java, Indonesia, Cilacap, Karanganyar, and Pati, as well as the factors determining the emissions. The DNDC model was obtained from <a href="https://www.dndc.sr.unh.edu/">https://www.dndc.sr.unh.edu</a>, which consists of three main submodels that worked together in simulating N<sub>2</sub>O and N<sub>2</sub> emissions: (1) the soil-climate/thermal-hydraulic flux submodel, (2) the decomposition submodel, and (3) the denitrification submodel. The results showed that the N<sub>2</sub>O emissions from rice farming in Karanganyar, Cilacap, and Pati were 19.0, 18.8, and 12.8 kg N ha<sup>−1</sup> yr<sup>−1</sup>, respectively, while they were 213.7, 270.6, and 360.6 kg C ha<sup>−1</sup> yr<sup>−1</sup> for CH<sub>4</sub> emissions, respectively. Consecutive dry or high precipitation, which resulted in cumulative depleted or elevated soil moisture, respectively, along with warmer temperature likely promoted higher methane and nitrous oxide. Experimental fields for validating the model in accordance with various agricultural practices are suggested for further study. Overall, the DNDC model has successfully estimated the CH<sub>4</sub> and N<sub>2</sub>O emissions in Central Java when incorporated with various secondary climatic and land management big data resources.</p>

Research on Library Personalized Push System Based on Big Data Decision

Big Data is the product of the rapid development and application of information technology, and it is also the inevitable trend of the development and application of intelligent technology. In the environment of big data, by giving full play to the advantages of big data decision-making system and actively constructing library personalized push system, we will break the foundation of Library Service Space and time, fully guarantee the information system and service ability of the library. In the process of operation, libraries should make full use of big data resources and accurately identify the service demand, reading behavior and individual characteristics of library users, innovate the Information Mode and personalized service method, continuously improve the user’s reading experience, improve the user relationship, thus form the accurate, personalized and intelligent book information service system.

Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform

The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.

A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data.

The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big data technology. This study places the collaborative ideological and political work of colleges and universities in the context of big data, and by analyzing its basic connotation and changes in the real situation, it explores the development progression of colleges and universities making full use of big data resources to cultivate a collaborative education model, which is conducive to promoting colleges and universities to cultivate a whole staff, whole process, and all-round accurate ideological education and value-led services and to shape excellent young college students with comprehensive growth. The first is to scientifically build a multilevel linked big data management platform for counselor professionalization construction, plan the technical architecture of the organizational platform, build a cloud database of counselor career files, and extract valuable information and data from the organizational activities at the macrolevel and personal activities at the microlevel with counselor professionalization construction activities; the second is to realize the integrated application of information resources for counselor team construction. The second is to realize the integrated application of counselor team construction information resources, visualise and accurately analyze and evaluate the counselor group's focus on career development and individual counselors' feedback on career capacity construction, and improve the overall construction, personalized education management level, and self-improvement development ability. Fourth, in the professionalization of counselors, attention should be paid to the scientific selection and prevention of risks of big data application, ensuring the authenticity and reliability of data and leakage prevention and control, etc.

Retraction Note: The abnormal precipitation in rainy season based on genetic algorithm and the security of big data in network

In this paper, cloudsim cloud simulation tool is studied, and its running environment is reasonably configured. Taking the platform as the background, the improved genetic algorithm proposed in this paper is used for simulation experiment that is to optimize the genetic algorithm through simulated annealing behavior. Through the simulation experiment of the improved genetic algorithm, it can be known that the genetic algorithm designed in this paper can satisfy multiple customers one by one and has a certain improvement effect on cloud service quality. In this paper, the problem of rainfall anomaly in city D is studied. The results show that the rainfall anomaly of city D is related to solar activity, 500hPa height field in winter and SST in the Central South Pacific from June to August of the previous year. It is found that there is almost no rainfall during the high solar activity years, while there is frequent rainfall near the low solar activity years. Finally, according to the homomorphic encryption mechanism, this paper studies the protection of data confidentiality in computer network security and the multistorage technology of big data. In this research scheme, by adding a certain number of virtual objects to two different computing information dummies, we successfully protect the data such as the number of items owned by the data owner, the content of the items owned, and the item objects called by the user. In addition, this paper uses bilinear mapping and RS coding technology to create related protocols to solve some problems, such as infinite verification of big data integrity, error data recovery, and protection of data owner’s computing resources. Through the design of solutions to the above problems, this paper successfully completed the protection of big data resources and network security based on cloud computing environment.

Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data.

Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of “common disease common variant hypothesis,” that suggests that if a disease that is heritable is common in the population (a prevalence >1–5%), then the genetic contributors—specific variations in the genetic code—will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.

USING OF COGNITIVE DYNAMIC SYSTEMS FOR INTELLIGENT TECHNICAL SYSTEMS CONTROL

For the second year around the world the impact of the coronavirus disease COVID-19 has been taking place. And, therefore, the modern world today is in a critical situation in the fight against a pandemic, where humanity is competing for its natural survival and has embraced the whole world, which greatly influenced the economic, social, and political aspects. The forecasts of specialists in various spheres of social life have versatile conclusions. But technological and informational components have received new development opportunities, and here it becomes interesting to consider the moment of creating a single information space, due to the effective implementation of the latest information resources that can transform ordinary services at a qualitatively new level. This is also the issue of virtualization of individual industries with high economic potential. Also, the integration of the latest technological solutions (issues of managing business systems at enterprises, organizing workplaces, abandoning institutions with reference to a specific building, etc.). When creating a remote form, Big Data resources become relevant, analysing large amounts of data and can issue their own conclusions and plan informed proposals. The conclusions obtained based on complex analytical technological systems based on artificial intelligence can compete with experts in a particular industry. Systems with a complex hierarchical system are capable of serious forecasting, but the focus of small companies on minor tasks does not give a comprehensive result on a national scale. Scientists and specialists involved in these tasks are limited in global information, therefore, the issue of attracting cognitive dynamic systems from the point of view of effective management is an urgent issue for solving artificial intelligence problems. In the work there are links to sources, explain the presented material.

Particular matter prediction using synergy of multiple source urban big data in smart cities

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

Big data analytics, resource orchestration, and digital sustainability: A case study of smart city development

Smart cities are expected to improve the efficiency and effectiveness of urban management, including public services, public security, and environmental protection, and to ultimately achieve Sustainable Development Goal (SDG) 11 for making cities inclusive, safe, resilient, and sustainable. Big data have been identified as a key enabler in the development of smart cities. However, our understanding of how different data sources should be managed and integrated remains limited. By analyzing data applications in the development of a sustainable smart city, this case study identified three phases of development, each requiring a different approach to orchestrating diverse data sources. A framework identifying the phases, data-related issues, data orchestration and its interaction with other resources, focal capabilities, and development approaches is developed. This study benefits both researchers and practitioners by making theoretical contributions and by offering practical insights in the fields of smart cities and big data.

Research on the Development Trend of Management Accounting in the Big Data Environment

Today, my country has entered the era of big data. In this context, my country’s management accounting has ushered in a new development opportunity. In the past, the development of accounting in my country was relatively backward, and it was not enough to support relevant staff to use comprehensive and objective information to improve and optimize management accounting. Nowadays, the development of big data enables relevant staff to obtain required information and relevant financial data through information technology and big data resources, and improve the work efficiency of relevant staff in solving management accounting problems.

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Since December 2019, the COVID‐19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative‐based approach of assessing the potential impact of the COVID‐19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision‐making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4–8% in the absence of COVID‐19, GDP growth in 2020 would be ‐8.77 to ‐12.77% after the COVID‐19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

RETRACTED ARTICLE: The abnormal precipitation in rainy season based on genetic algorithm and the security of big data in network

In this paper, cloudsim cloud simulation tool is studied, and its running environment is reasonably configured. Taking the platform as the background, the improved genetic algorithm proposed in this paper is used for simulation experiment that is to optimize the genetic algorithm through simulated annealing behavior. Through the simulation experiment of the improved genetic algorithm, it can be known that the genetic algorithm designed in this paper can satisfy multiple customers one by one and has a certain improvement effect on cloud service quality. In this paper, the problem of rainfall anomaly in city D is studied. The results show that the rainfall anomaly of city D is related to solar activity, 500hPa height field in winter and SST in the Central South Pacific from June to August of the previous year. It is found that there is almost no rainfall during the high solar activity years, while there is frequent rainfall near the low solar activity years. Finally, according to the homomorphic encryption mechanism, this paper studies the protection of data confidentiality in computer network security and the multistorage technology of big data. In this research scheme, by adding a certain number of virtual objects to two different computing information dummies, we successfully protect the data such as the number of items owned by the data owner, the content of the items owned, and the item objects called by the user. In addition, this paper uses bilinear mapping and RS coding technology to create related protocols to solve some problems, such as infinite verification of big data integrity, error data recovery, and protection of data owner’s computing resources. Through the design of solutions to the above problems, this paper successfully completed the protection of big data resources and network security based on cloud computing environment.

Establishing a second-generation artificial intelligence-based system for improving diagnosis, treatment, and monitoring of patients with rare diseases.

Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients' disease and response to therapy.

A Brand-New Look at You: Predicting Brand Personality in Social Media Networks with Machine Learning

Tools for analyzing social media text data to gain marketing insight have recently emerged. While a wealth of research has focused on automated human personality assessment, little research has focused on advancing methods for obtaining brand personality from social media content. Brand personality is a nuanced aspect of brands that has a consistent set of traits aside from its functional benefits. In this study, we introduce a novel, automated, and generalizable data analytics approach to extract near real-time estimates of brand personalities in social media networks. This method can be used to track attempts to change brand personality over time, measure brand personality of competitors, and assess congruence in brand personality. Applied to consumer data, firms can assess how consumers perceive brand personality and study the effects of brand–consumer congruence in personality. Our approach develops a novel hybrid machine learning algorithmic design (LDA2Vec), which bypasses often extensive manual coding tasks, thus providing an adaptable and scalable tool that can be used for a range of management studies. Our approach enhances the theoretical understanding of channeled and perceived brand personality as it is represented in social media networks and provides practitioners with the ability to foster branding strategies by using big data resources.

The Prediction of Venture Capitalists' Investment Propensity Using Machine Learning

This paper describes the most visible data science methods suitable for entrepreneurial research and provides links to literature and big data resources for venture capitalists. In the results, first, all organizational characteristics such as the characteristic of parent company of VC, the fund size of VC, and the reputation of VC, have significant influences on the risk-taking investment of venture capitalists, while functional background, school prestige, and VC experience except educational level among individual characteristics have significant influences on the risk-taking investment of venture capitalists. Second, for the full model, the accuracy rate is 0.855, which implies that the error rate is 0.145. Among the venture capitalists who are predicted not to do risk-taking investment, the accuracy that would not do risk-taking investment is 85.75%, and the accuracy that do risk-taking investment is 79.59% among the venture capitalists who are predicted to do risk-taking investment.

Visualizing risk factors of dementia from scholarly literature using knowledge maps and next-generation data models

Scholarly communication of knowledge is predominantly document-based in digital repositories, and researchers find it tedious to automatically capture and process the semantics among related articles. Despite the present digital era of big data, there is a lack of visual representations of the knowledge present in scholarly articles, and a time-saving approach for a literature search and visual navigation is warranted. The majority of knowledge display tools cannot cope with current big data trends and pose limitations in meeting the requirements of automatic knowledge representation, storage, and dynamic visualization. To address this limitation, the main aim of this paper is to model the visualization of unstructured data and explore the feasibility of achieving visual navigation for researchers to gain insight into the knowledge hidden in scientific articles of digital repositories. Contemporary topics of research and practice, including modifiable risk factors leading to a dramatic increase in Alzheimer’s disease and other forms of dementia, warrant deeper insight into the evidence-based knowledge available in the literature. The goal is to provide researchers with a visual-based easy traversal through a digital repository of research articles. This paper takes the first step in proposing a novel integrated model using knowledge maps and next-generation graph datastores to achieve a semantic visualization with domain-specific knowledge, such as dementia risk factors. The model facilitates a deep conceptual understanding of the literature by automatically establishing visual relationships among the extracted knowledge from the big data resources of research articles. It also serves as an automated tool for a visual navigation through the knowledge repository for faster identification of dementia risk factors reported in scholarly articles. Further, it facilitates a semantic visualization and domain-specific knowledge discovery from a large digital repository and their associations. In this study, the implementation of the proposed model in the Neo4j graph data repository, along with the results achieved, is presented as a proof of concept. Using scholarly research articles on dementia risk factors as a case study, automatic knowledge extraction, storage, intelligent search, and visual navigation are illustrated. The implementation of contextual knowledge and its relationship for a visual exploration by researchers show promising results in the knowledge discovery of dementia risk factors. Overall, this study demonstrates the significance of a semantic visualization with the effective use of knowledge maps and paves the way for extending visual modeling capabilities in the future.