Big Data Integration Research Articles

Integration of big data in the decision-making process in the real estate sector

We live in a time when the volume of data is growing at an unprecedented rate every day. Technical progress and the emergence of big data have transformed the development of all sectors. Although it came with its challenges it also created new opportunities. The emergence of Big Data and related analytics technologies led to changes in the business world. In today’s business processes, extracting genuine value form Big Data, and integrating it into the core decision-making strategy plays a vital role. Any company that wants to stay competitive in its business sector has to adopt to the changes. Data analytics is transforming the way companies do business by deriving value from large data sets. Real value from big data and integrating it into core solutions - strategy development is vital. The article studies the process of integration of big data into the decision-making process in the real estate sector. The authors examine the role of big data for decision-making and identify the importance of big data in various decision-making processes in the real estate sector. The article can be seen only as a brief introduction. The results of this study can be used for further analysis on this topic.

Research on the Integration of ESP and Big Data in Modern College English Teaching

In the context of big data, it analyzes ESP-based college English teaching. First, it explains the big data theory in ESP, and then discusses the resource discovery service, information storage service, resource organization service, and information retrieval service of English teaching. On the basis of the service content of ESP in college English teaching, it expounds the self-learning mode of college English based on ESP theory from the aspects of one-stop resource service, subject knowledge service, information visualization service and smart service.

Research on Enterprise Human Resource Management Innovation in Big Data Era

With the rapid development of computer technology, the emergence of new media and intelligence has changed the traditional way of production and life. While bringing convenience to us, it also produces massive data. Therefore, big data technology emerges as the times require. Through the data integration and analysis ability of big data, it has an important impact on the development of enterprises, especially in the management of human resources, higher requirements. Therefore, this paper puts forward the research of human resource management innovation in the era of big data. This paper makes a detailed study on the traditional human resource management mode of enterprises, and finds that the existing management mode has obvious shortcomings such as incomplete overall planning and unreasonable performance appraisal. The big data technology can make up for these shortcomings. Based on big data technology, this paper puts forward an innovative attendance analysis algorithm. According to the actual needs of the existing enterprise human resource management, combined with the advantages of big data analysis, the algorithm solves the unreasonable problem of existing attendance analysis. In order to further verify the actual effect of big data human resource management mode, relevant investigation and evaluation are carried out. According to the survey results, the enterprise human resource management mode based on big data technology has been significantly improved in rationality, fairness, scientificity and so on, which can better meet the needs of enterprises for human resource management.

Retraction: Research on the Integration of ESP and Big Data in Modern College English Teaching (J. Phys.: Conf. Ser. 1852 022046)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 9 September 2022

RETRACTED ARTICLE: Impact of PM2.5 environmental regulation based on big data for green technology development

China has received widespread attention in recent years due to the severe PM2.5 problem. Although paying close attention to the government, the government felt immense pressure to adopt a range of corrective measures and policies, but the impact of these measures and policies was not clear and citizens were not happy with the governance status of PM2.5. The biggest issue with PM2.5 management is the lack of environmental law enforcement. Many emerging technologies have arisen as a result of economic growth, and these technologies play a major role in gathering information and processing information and enhancing the quality of information. Therefore, we analyzes and explores the emerging ideas and approaches of these technologies and assesses the evolution of these technologies. It is obvious that the combination of big data technology and industrial green manufacturing technology is slow, and it is necessary to combine industrial green manufacturing enterprises and big data technology with artificial intelligence. In order to improve the current severe environmental problems, we have discussed technical advances and improved business efficiency such as development trend of green technology production based on big data technology, integration and innovation of big data technology and green technology, enriching environmental regulatory forms and participating entities, and PM2.5 environmental regulation strategy and recommendations for green technology advancement based on big data.

International Competitiveness of Jilin Province Automobile Industry Based on Big Data and Low-Carbon Economy

With the rapid development of economic globalization at this stage, the ecological environment of the earth has become one of the hot issues of global concern. As the largest developing country in the world, our country faces the highest carbon emissions and increase in the automotive industry. From the perspective of the integration of big data and low-carbon economy, the international competitiveness of the automobile industry in Jilin Province has also been continuously improved. Based on the situation in Jilin Province, this paper constructs a comprehensive carbon emission research framework suitable for the low-carbon economic automobile industry in Jilin Province. Through this framework, the automobile industry of our country’s Jilin Province can be improved internationally from the perspective of low-carbon economy. This article first analyzes and elaborates the theory of low-carbon concept, and through the theoretical research foundation of low-carbon economy, studies the theoretical foundation of Jilin Province’s automobile industry’s transition to low-carbon economy from the perspective of big data. Secondly, it analyzes the status quo of Jilin Province. Through the use of the extended model of kaya identity and the LMDI logarithmic decomposition method, based on the carbon emissions data of the automobile industry in Jilin Province from 2015 to 2020, the quantitative analysis of energy intensity and economic growth. The impact of five factors, including population, automobile industry structure and energy structure, on carbon emissions in Jilin Province from the perspective of low-carbon economy, will be studied in depth. Finally, the peak value of Tai emission in Jilin Province is calculated, the IPAT model is constructed using Eviews software, and the prediction formula of carbon emission is obtained. The value of carbon emission in Jilin Province is simulated by Matlab software and compared with foreign countries. The experimental research results show that the use of big data analysis technology for technological development in the automotive industry to achieve low-carbon economic development has greatly promoted the competitiveness of Jilin Province’s automotive industry in the international industry.

Big data and industrial innovation progress in Jiangxi Province incremental effect highlights enabling digital economy cultivation

With the advent of the Internet information age, most social and economic activities rely on data. As a new resource, big data technology plays an important role in the social digital economy. Therefore, it is not only the objective requirement of economic development, but also the value of big data resources to promote the close integration of big data and industry, and help the upgrading and development of industry with big data. In order to emphasize the strength of digital economy, this paper studies the gradual effect of big data and industrial innovation in Jiangxi Province, and carries out a series of studies. With the development of the times, the most popular Internet also changes with the development of the times. The Internet has changed from the IT era at the beginning to the new digital technology era. The creation of big data technology and the establishment of database have become the storage and source direction of new resources for all industries. At present, the digital economy is facing the opportunities and challenges of the “Internet plus” new market. Digital economy related enterprises should strengthen mutual cooperation among resources supply network organizations such as information and information, dissolve and reorganize production factors, strengthen the competitiveness among industrial economies, promote the optimization and upgrading of industrial economic structure, and expand the market space for which the industry can develop. However, the development of the Internet plus industry depends on the innovative investment and innovation power of the industry. In the era of big data, one of the ways to improve the overall level of the industry is innovation. This paper discusses how much influence big data shows in the cultivation of digital economy.

Information Management Strategy of Bridge Construction Technology Based on Big Data

With the rapid integration of big data, cloud computing, BIM and other new technologies into construction enterprises, the degree of information of bridge construction is gradually deepened. This process is usually very complex, there are many influencing factors, and the construction progress is difficult to control. This paper summarizes the fiber composite materials which have potential application in Bridge Engineering in recent years. This paper first discusses the development prospect of carbon fiber, and then briefly introduces the mechanical properties of carbon fiber composite laminates. Referring to relevant literature, this paper introduces BIM Technology in detail.Based on the current situation and application advantages of BIM technology,it fully understands the practical application of BIM Technology in bridge engineering design, construction stage and later operation and maintenance. Their tensile strength, elastic modulus, longitudinal and transverse thermal expansion are introduced in detail.The results show that BIM Technology has been widely used in bridge engineering projects. In 2015, the Growth rate of BIM technology usage in construction industry country a was 1.536.

Efficient Continuous Big Data Integrity Checking for Decentralized Storage

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

Federal funding allocation on HIV/AIDS research in the United States (2008–2018): an exploratory study using Big Data

ABSTRACT Literature suggests that federal funding allocation for HIV-related research in the US may not align with HIV disease burden but is influenced by structural disparities. This study sought to examine how federal funding allocation is associated with HIV disease burden and research capacity of states by applying Big Data integration, text mining, and statistics. Using text mining, we identified 20,678 HIV-related federal projects from 2008 to 2018 in NIH ExPORTER, which were then integrated with data from AtlasPlus and US Census Bureau. We developed Gini coefficients to assess the inequality of funding and the Generalized Estimating Equations model to examine the associations between funding allocation and (1) state HIV disease burden, (2) state research capacity, and (3) geographic regions, respectively. The Gini coefficients (0.60 to 0.80) suggest a highly skewed funding distribution. Funding allocation was not associated with state HIV disease burden (p = 0.269) but HIV research capacity (p = 0.000). The South (with the heaviest HIV disease burden) did not receive significantly more federal funding. Our findings for the first time identified disparities of federal funding allocation, suggesting that federal agencies favor states of high research capacity over heavy disease burden, which may reinforce the HIV-related health disparities.

Machine intelligence identifies soluble TNFa as a therapeutic target for spinal cord injury

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target.

Semantic-based Big Data integration framework using scalable distributed ontology matching strategy

Nowadays, Big Data management has become a key basis for innovation, productivity growth, and competition. The correlated exploitation of data of this magnitude remains primordial to discover valuable insights and support decision making for domains of major interest. Furthermore, despite the complex aspects of Big Data environments, users are usually looking for a unified and appropriate view of this huge and heterogeneous data, to support the extraction of reliable and consistent knowledge. Thus, Big Data integration mechanisms must be considered to provide a uniform query interface, to mediate across large datasets and provide data scientists with a consistent integrated view suitable for analytical exploitations. Thus, this paper presents a semantic-based Big Data integration framework that relies on large-scale ontology matching and probabilistic-logical based assessment strategies. This framework applies optimization mechanisms and leverages parallel-computing paradigms (Hadoop and MapReduce) using commodity computational resources, to efficiently address the Big Data challenges and aspects. Several experiments were conducted and have proven the efficiency of this framework in terms of accuracy, performance, and scalability.

Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.

Big data-enabled large-scale group decision making for circular economy: An emerging market context

This study is focused on presenting a unique landscape for big data-enabled circular economy that involves stakeholders as important decision makers. This research is designed based on five case studies from emerging markets with a focus on circular models to propose a framework for large scale decision making. In these cases, different linear economy problems are addressed that further utilizes the integration of big data and large-scale group decision making by stakeholders to achieve circularity. The findings of our study indicate a four-step design (enabling technologies, business significance, deriving value, and circular goals) to implement the 10R's of the circular economy through emerging technologies such as big data and related mobile applications along with cloud-based platforms. The study highlights how cases from emerging markets can be useful for other firms and ecosystems, ranging from e-commerce to manufacturing, that employ large number of decision makers with the aim of creating a circular economy. At the end, the study presents theoretical and practical implications along with the scope for future research.

RETRACTED: Research on the construction of university education information platform supported by cloud computing

The level of information construction in Colleges and universities is an important indicator to reflect the level of education in Colleges and universities. With the development of cloud computing technology, how to solve the above problems and how to use cloud computing to improve the university education information platform is a problem that the builders of university education information platform should think deeply. This paper first analyzes the current situation of university education information cloud platform, and then puts forward the education information scheme based on cloud computing, and finally realizes the education information system based on cloud computing. The system promotes the integration of cloud computing, big data and other cutting-edge technologies with the education industry, and improves the work efficiency of relevant personnel in Colleges and universities.

Approaches for the integration of big data in translational medicine: single-cell and computational methods.

Translational medicine describes a bench-to-bedside approach that eventually converts findings from basic scientific studies into real-world clinical research. It encompasses new treatments, advanced equipment, medical procedures, preventive and diagnostic approaches creating a bridge between basic studies and clinical research. Despite considerable investment in basic science, improvements in technology, and increased knowledge of the biology of human disease, translation of laboratory findings into substantial therapeutic progress has been slower than expected, and the return on investment has been limited in terms of clinical efficacy. In this review, we provide a fresh perspective on some experimental and computational approaches for translational medicine. We cover the analysis, visualization, and modeling of high-dimensional data, with a focus on single-cell technologies, sequence, and structure analysis. Current challenges, limitations, and future directions, with examples from cancer and fibrotic disease, will be discussed.

Review on Integrating Geospatial Big Datasets and Open Research Issues

Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.

Integration of Cloud Computing, Big Data, Artificial Intelligence, and Internet of Things

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors center their attention on the integration of cloud, IoT, big data, and artificial intelligence. Several kinds of research papers have surveyed artificial intelligence, cloud, IoT, and big data separately and, more precisely, their main properties, characteristics, underlying technologies, and open issues. However, to the greatest of the authors' knowledge, these works require a detailed analysis of the new paradigm that combines the four technologies, which suggests completely new challenges and research issues. To bridge this gap, this paper presents a survey on the integration of cloud, IoT, artificial intelligence, and big data.

Research on Big Data Integration Method for Investment Statistics Based on Artificial Intelligence Technology

Aiming at the problems of big data resources of investment statistics in the management process, such as unstructured, massive, and multi-type, the research on the big data integration method for investment statistics based on artificial intelligence technology is conducted. A new method of big data integration is proposed through the correlation analysis of investment statistics big data, the retrieval of investment statistics information resources, and the autonomous integration of big data based on artificial intelligence technology. Experiments have proved that compared with traditional integration methods, this method can effectively reduce the loss of information resources, ensure the integrity of the integrated investment statistics big data resources, and provide theoretical and technical explorations for the compilation of enterprise investment statistics, also provide a reference for big data integration in other related fields.

Discussion on the Integration and Application of Artificial Intelligence, Big Data and Cloud Computing

With the development of the Internet and technology, a revolution in information technology is taking place. Artificial intelligence, big data and cloud computing have been applied in various fields, promoted social progress and brought new growth points for economic development. Artificial intelligence, big data, and cloud computing are independent, mutually reinforcing, and complementary to each other. They are also gradually merging from a single development direction to a three-dimensional integration, forming the "ABC Golden Triangle." In the future, the integrated development of the three is an inevitable development trend, and the integrated application of the three will also be reflected in all walks of life. Based on it, this article discusses the integration, application fields and future development trends of artificial intelligence, big data, and cloud computing.