Search

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

The peculiar nonlinear optical responses of two-dimensional (2D) materials endow them highly attractive for photonics applications. This review systematically discusses the performance of pulsed lasers employing 2D materials as their modulation device. First, it is crucial to categorize 2D materials that can act as saturable absorbers, encompassing a range of substances such as Graphene, Black Phosphorus, Topological Insulators, MXene, and even heterojunction. Furthermore, a thorough investigation is conducted to analyze the performance indexes to study the nonlinear characteristics of 2D materials in the pulse generation. Especially, the focus is on the 1.5 μ m band ultrafast lasers utilizing 2D materials. This paper also provides a comprehensive analysis of the obstacles and prospects in 2D materials preparation, and their utilization in the pulse laser systems. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has triggered a global public health crisis, affecting hundreds of countries. With the increasing number of infected cases, developing automated COVID-19 identification tools based on CT images can effectively assist clinical diagnosis and reduce the tedious workload of image interpretation. To expand the dataset for machine learning methods, it is necessary to aggregate cases from different medical systems to learn robust and generalizable models. This paper proposes a novel deep learning joint framework that can effectively handle heterogeneous datasets with distribution discrepancies for accurate COVID-19 identification. We address the cross-site domain shift by redesigning the COVID-Net's network architecture and learning strategy, and independent feature normalization in latent space to improve prediction accuracy and learning efficiency. Additionally, we propose using a contrastive training objective to enhance the domain invariance of semantic embeddings and boost classification performance on each dataset. We develop and evaluate our method with two large-scale public COVID-19 diagnosis datasets containing CT images. Extensive experiments show that our method consistently improves the performance both datasets, outperforming the original COVID-Net trained on each dataset by 13.27% and 15.15% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods. [ABSTRACT FROM AUTHOR]

Consider a simple graph G = 〈V, E〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, ..., n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k. In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K2n - {ne} into n (n - 1) 2 edge distinct copies of C4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. [ABSTRACT FROM AUTHOR]

At present, there is little research on the application of wireless sensor networks in the agricultural field. In order to improve the operating performance and practical effects of the intelligent agricultural system, based on data mining technology, this paper uses ZigBee wireless sensor network as the networking technology to cover all aspects of crops under the guidance of the concept of sustainable agricultural development, and realizes the data collection and remote-control process of the agricultural production process, and conducts data analysis and processing through data mining. Moreover, in order to improve the performance of the model, this paper proposes to use the single-point crossover multiple-generation genetic algorithm to optimize the weights and thresholds of the BP neural network to establish the Multi-Generation Genetic Algorithm Back Propagation MGABP model. In addition, in application, the analytic hierarchy process is introduced as the guidance mechanism of neural networks. Finally, this paper designs experiments to analyze the performance of the system constructed in this paper, and uses mathematical statistics to perform statistics on experimental results. The experimental analysis and statistical diagrams of various parameters shows the outcome of this study. The research results show that the intelligent agricultural system model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis. [ABSTRACT FROM AUTHOR]

The integrated development of culture and tourism public services can better enhance the value of scenic spots, promote the spread of Chinese excellent culture, and drive the development of tourism economy. Big data technology can predict the future development law of transactions based on historical data. It can meet the needs of tourists and tourism enterprises in a targeted manner. This paper aims at the integrated development practice of tea culture and tourism public services in the era of big data, analyzes the influencing factors of the integrated development of tea culture tourism and public services in Tianfu Tea Garden and the problems existing in the development process of Tianfu Tea Garden, and designs data analysis algorithms through big data technology, and put forward development suggestions combined with big data technology, mainly including improving the industrial chain; improving the overall development scope of the scenic spot; strengthening the quality training of tea garden employees; improving infrastructure; highlighting the connotation of tea culture in scenic spots; developing and researching new tourism products; increase the publicity and construction of scenic spots, so as to provide reference for the development of tea culture and tourism in Tianfu Tea Garden. [ABSTRACT FROM AUTHOR]

As increasing attention is given to the impact of the built environment on citizens' well-being and health, the role and performance of urban parks have to be carefully considered. The research presented in this paper focuses on the efficiency of urban parks concerning their visitor flow and explores an aspect that has not been studied so far: the impact of the built environment surrounding urban parks in attracting visitors to parks. To assess the visitor flow, the research took advantage of the large amount of accessible and accurate data connecting users and spaces: points of interest data (POI) data and Weibo check-in data. The research correlated them to investigate how the land uses and their mix in a buffer area of 400 m around each park affect the quantity of visitor flow into the parks of the city of Suzhou, China. The research first quantified the POI mixed degree of land uses and the visitor quantity based on the Hill Number Index and the social media data; then, it analyzed the sensitivity of different types of parks to different types of POI. After clarifying the quadratic function between visitor intensity and the POI mixed degree, the research found: (1) a clear correlation between the visitor intensity and the land-use mix around the parks; and (2) that different kinds of parks are affected by different groups of uses. Specifically, in the case of community parks, the visitor flow is affected by the land use for daily activities, while the visitor flow of comprehensive parks is affected by uses such as offices and services. These findings provide clear indications for improving urban parks and land-use efficiency. [ABSTRACT FROM AUTHOR]

Online misogyny is growing at an alarming rate, constituting a violent backlash against feminist activism for gender equality. In our paper, we analyze misogynistic discourses on Twitter generated by #MGTOW (men going their own way) using Thick Big Data. This mixed research method involved a quantitative analysis of 167,582 tweets with #MGTOW and #feminism, followed by a qualitative study of 1,000 tweets of both hashtags. Our study reveals that despite the official narrative of MGTOW as a separatist community of men "going their own way," #MGTOW's central goal is in fact the fight against gender equality. The quantitative and qualitative analysis of the language, sentiment, tone, referred sources, and comparisons between #MGTOW and #feminism show that #MGTOW does not simply voice a separatist approach towards women but promotes violence against women and feminism. While feminist tweets are more oriented toward the creation of common identity by referring to shared values and having an internal focus, MGTOW tweets express opposition to "others" and emphasize an "us vs. them" mentality. Our study also shows that online misogyny is something larger than its common definition as a violent anti-women expression in digital environments. It is a defense of a patriarchal system that allows men to claim gender, race, and other kinds of privileges to which they feel entitled. [ABSTRACT FROM AUTHOR]

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers. [ABSTRACT FROM AUTHOR]

A distributed firewall is a security application that monitors and controls traffic on an organization's network. While centralized firewalls are used against attacks coming from outside a network, distributed firewalls are considered for inside attacks from internal networks such as wireless access and VPN tunnel. Distributed firewalls use policies, which are stated by rules, to find anomalous packets. However, such static rules may be incomplete. In this case, by monitoring firewall logs, the anomalies can be detected. Such logs become big when networks have high traffic, but their hidden knowledge contains valuable information about existing anomalies. In this paper, to detect the anomalies, we extract patterns from big data logs of distributed firewalls using data mining and machine learning. The proposed method is applied to big logs from distributed firewalls in a real security environment, and results are analyzed. [ABSTRACT FROM AUTHOR]

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors' streaming data analysis. [ABSTRACT FROM AUTHOR]

In recent years, global warming has become increasingly severe, and the ecological and environmental problems facing mankind have become increasingly serious. As the main areas of transportation activities, cities are also the main places of carbon emissions. As a necessary condition for human's daily-life travel, it is particularly important to calculate the carbon emissions from urban transportation. Due to the different characteristics of economy and population in different regions of a city, the carbon emissions of urban buses show different characteristics in terms of temporal and spatial distribution. The developments of science and technology promote the application of big data analysis to specific practical life, enabling people to research and solve problems from a new perspective. This paper uses the GPS data of urban buses in Sanya City, China, to identify operation conditions from urban buses, and calculates the distance and time under different conditions. Based on the measured data of carbon emissions, this paper visualizes the distribution characteristics of carbon emissions by density analysis; explains the time distribution characteristics by the visual analysis of carbon emissions in different time periods, working days and rest days, and different energy types; and illustrates the spatial distribution characteristics by the spatial distributions of carbon emissions from Sanya's buses on working days and rest days, as well as in different routes, providing reference for a low-carbon development of urban green transport. [ABSTRACT FROM AUTHOR]

Nowadays, several companies prefer storing their data on multiple data centers with replication for many reasons. The data that spans various data centers ensures the fastest possible response time for customers and workforces who are geographically separated. It also provides protecting the information from the loss in case a single data center experiences a disaster. However, the amount of data is increasing at a rapid pace, which leads to challenges in storage, analysis, and various processing tasks. In this paper, we propose and design a geographically distributed data management framework to manage the massive data stored and distributed among geo-distributed data centers. The goal of the proposed framework is to enable efficient use of the distributed data blocks for various data analysis tasks. The architecture of the proposed framework is composed of a grid of geo-distributed data centers connected to a data controller (DCtrl). The DCtrl is responsible for organizing and managing the block replicas across the geo-distributed data centers. We use the BDMS system as the installed system on the distributed data centers. BDMS stores the big data file as a set of random sample data blocks, each being a random sample of the whole data file. Then, DCtrl distributes these data blocks into multiple data centers with replication. In analyzing a big data file distributed based on the proposed framework, we randomly select a sample of data blocks replicated from other data centers on any data center. We use simulation results to demonstrate the performance of the proposed framework in big data analysis across geo-distributed data centers. [ABSTRACT FROM AUTHOR]

Coronavirus disease-19 (COVID-19), an infectious disease that spreads when people live in close proximity has greatly impacted healthcare systems worldwide. The pandemic has so disrupted human life economically and socially that the scientific community has been impelled to devise a solution that assists in the diagnosis, prevention and outbreak prediction of COVID-19. This has generated an enormous quantum of unstructured data that cannot be processed by traditional methods. To alleviate COVID-19 threat and to process these unstructured data, big data analytics can be used. The main objective of this paper is to present a multidimensional survey on open source datasets, techniques and tools in big data to fight COVID-19. To this end, state-of-the-art articles have been analyzed, qualitatively and quantitatively, to put together a body of work in the prediction of COVID-19. The findings of this review show that machine learning classification algorithms in big data analytics helps design a predictive model for COVID-19 using the open source datasets. This survey may serve as a starting point to enhance the research in COVID-19. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight–communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV's flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission's target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV's cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication. [ABSTRACT FROM AUTHOR]

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., "neutral" sentiment was present in most of the Tweets. It was followed by "negative" and "positive" sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset. [ABSTRACT FROM AUTHOR]

Resilient supply chains have become a key issue for manufacturing companies to ensure a stable supply for their manufacturing processes and for governments to ensure the stable supply of essential goods to society. Building diversified supply chains and monitoring the performance of suppliers are major mitigation strategies to counteract disruptions at an early stage. Supply chain risk management and monitoring of supply chains using big data analytics are getting increasing attention. The growing number of data sources has huge implications on the reporting of incidents that may disrupt supply chains. The data sources may stem from a variety of internet sources like daily media reports provided on websites, social media or specialist media, or they may stem from specific databases. The sooner this information is disclosed to stakeholders and analysed the better the preventive strategies generally are. Timely information prolongs the reaction time and may help to reduce the severity of an incident. This paper highlights a science-based real-time tracking analysis of risks in the mineral raw material markets for the period 2019 to 2021 using big data analytics provided by a commercial system. A set of data for 12 selected mineral raw materials was provided by the authors and analysed using more than 100 risk indicators from 14 major risk categories as part of a commercial big data system. The extracted information can have imminent value to identify supply shortages, production halts or price peaks at an early stage. The main question was to find out whether such big data analytics are precise enough to detect potential, globally relevant, supply shortages in mineral raw material markets in due time. The results of this paper show that using big data analytics can be a very effective tool to extract relevant information about supply sources and to react timely in case of disruptions or social or environmental mismanagement on the supplier side. However, the nature of big data sources suggests that the parameters of the applied models need elaborate configuration. Each raw mineral market has its own peculiarities in terms of volume, mode of transport, market concentration or countries of origin. These factors influence the relevance of the reported incidents. Furthermore, some incidents have a spurious or only minor connection to the individual markets. For these reasons, we conclude that only supervised models reap the most benefits in the monitoring of mineral raw material markets. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm that offers remarkable opportunities for data mining and analysis. IoT envisions a world where all smartphones, vehicles, public services facilities, and home appliances that can be connected to the internet act as data sources. Even today, a significant portion of electronic devices, including watches, emergency alarms, parking doors, and many appliances can be linked to IoT systems and remotely controlled. Big data analysis and data mining methods can be utilized to improve the performance of IoT systems and address their challenges in the area of data storage, processing, and analysis. Extensive studies on IoT with big data can make it possible to accumulate tremendous data and transform it into valuable knowledge using data mining techniques. With this background, this paper provides a systematic survey of the literature on the use of big data analytics and data mining methods in IoT. This review aims to identify the lines of research that should receive more attention in future works. To achieve this goal, the articles published between 2010 and 2021 on the subjects of IoT-based big data and IoT-based data mining (60 articles) have been reviewed. These articles fall into four general categories in terms of focus: architecture/platform, framework, applications, and security. The paper provides a summary of the methods used in IoT-based big data analysis and IoT-based data mining in these four categories to highlight the promising avenues of research for future works. [ABSTRACT FROM AUTHOR]

In the context of the continuous promotion of China's big data development strategy, this paper quantitively analyses China's existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China's big data policies. This paper carries out a quantitative textual analysis of China's national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies. [ABSTRACT FROM AUTHOR]

In order to measure the quality and level of digital transformation of banks, we evaluate its credibility. Due to the backwardness of data processing technology, the existing credibility evaluation methods have the defects of low validity of evaluation results, which indicates that the evaluation results are one-sided and can not meet the needs of today's digital transformation of banks. Therefore, this paper proposes an intelligent evaluation method for the credibility of digital transformation of banks based on big data analysis. This paper analyzes the digital transformation strategy of banks in detail. On this basis, it introduces big data analysis technology, selects credibility intelligent evaluation index, constructs credibility intelligent evaluation model, formulates credibility intelligent evaluation steps of digital transformation of banks according to the construction model, and implements the steps to realize the intelligent evaluation of digital transformation credibility of banks. The experimental data show that: compared with the existing methods, the validity data of the evaluation results of the proposed method is larger, which fully confirms the effectiveness and feasibility of the proposed method and is suitable for vigorously promoting and applying. [ABSTRACT FROM AUTHOR]

The rapid development of Internet technology has promoted the vigorous development of the multimedia. As one of the most classic instruments, the violin has been fully developed in its creation, education, and performance. In the face of more and more violin performances, the effective organization and retrieval of these musical works is an urgent problem to be solved, while it is common to classify and organize music based on the emotional properties of the performance. Deep learning is a model based on feature hierarchy and unsupervised feature learning, which has strong learning ability and adaptability. Based on the recurrent neural network (RNN) method, long short-term memory (LSTM) is one of the classic models of deep learning that can effectively learn the characteristics of time series data and achieve effective predictions. Therefore, based on the classical Hevner emotion classification model, this paper proposes an emotion recognition method for dynamic violin performances based on LSTM, which selects acoustic features and classifies the audio acoustic signals contained in the violin performances. In order to verify the effectiveness of this method, this paper carries out data labeled, feature selection, and model test on the actual violin music data by turn. The results show that the proposed method can greatly reduce the training time and improve the prediction accuracy, which reached 83%, higher than the existing methods. Meanwhile, the accuracy and iteration times of violin playing music of different emotional categories are also counted. Moreover, the method is robust to the genre, timbre, and noise changes, and the emotion recognition effect is superior. [ABSTRACT FROM AUTHOR]

Actively responding to population aging has become the new national condition of China's aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China's aging society. From the perspective of big data technology, this paper systematically studies the concerns of China's aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China's aging, which indirectly illustrates the applicability of big data analysis to the concerns of China's aging. [ABSTRACT FROM AUTHOR]

In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students' innovation and entrepreneurship education is analyzed, and the optimization model of college students' Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students. [ABSTRACT FROM AUTHOR]

Based on virtual reality technology, landscape 3D modeling provides users with the possibility to construct a simulated garden landscape environment design effect online, so it has high requirements for accuracy. With the continuous improvement of precision requirements, the number of people involved in the construction of 3D models is also increasing, which puts forward higher requirements for modeling. Based on this, this paper studies the optimization strategy of landscape space 3D model based on big data analysis. Based on the analysis of the establishment of the 3D model and the related algorithm research, this paper analyzes the optimal design of the 3D model under the background of big data. In the 3D modeling of the edge folded area, it is based on the traditional quadratic error measurement grid simplification algorithm, combined with the vertex error matrix to simplify, so as to shorten the modeling time. Based on an efficient search algorithm, an adaptive nonsearch fractal image compression and decoding method is proposed in the image compression and decoding stage of 3D modeling. The search is performed by specifying the defined area block. Finally, an experiment is designed to analyze the performance of the optimization algorithm. The results show that the improved edge folding region algorithm can reduce errors on the basis of ensuring image quality, and the adaptive search algorithm can shorten the search time and improve the compression rate. This method provides a technical reference for the visualization experience and simulation system of garden landscape design and improves the presentation quality of virtual garden landscape design scenes. [ABSTRACT FROM AUTHOR]

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow. [ABSTRACT FROM AUTHOR]

The advent of big data has transformed the way in which businesses interact with consumers. With the ability to collect and analyze vast amounts of data from multiple sources, companies are now able to gain insights into consumer behavior that were previously impossible. This paper explores the relationship between big data, consumer behavior, digital marketing and personalizing the customer experience. It examines the ways in which big data is being used to gain insights into consumer behavior, the benefits and limitations of using big data, ethical considerations and the role of digital marketing in leveraging big data to create more effective marketing strategies. [ABSTRACT FROM AUTHOR]

Outliers and extreme values are common in the era of big data, especially in the collection of survey data and real analysis. Clearly, care needs to be taken with how such values are treated in the calculation of statistical summaries, such as those involving the sample mean and sample variance. Robust alternatives based on trimming or Winsorization are often employed to mitigate the effect of those outlying points. An aspect critical to these methods, however, is in the determination of the cutoff locations. One classic approach is g-and-g-times trimming/Winsorization, which takes a proportion g off from both tails. However, this method does not carry any confidence statement, such as one finds with the calculation of statistical intervals. We propose the application of nonparametric statistical tolerance intervals, which captures a specified proportion of the sampled population at a confidence level, to determine cutoff locations for trimming and Winsorization. Extensive simulation studies show that this approach yields better coverage than the g-and-g-times method, even though the latter was not designed as a confidence procedure. Census of Agriculture data since 1982 is analyzed to highlight the impact on statistical summaries regarding farm land. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

With the rise of "Internet+" as a national strategy, traditional industries have accelerated their integration with the Internet, and the education industry has also begun to seek innovative development. The fast-paced life of modern people makes time for learning more fragmented, and the high pressure of life makes people want to avoid excessive pressure from learning. These give opportunities and advantages to the development of online education, which is more adaptable to fragmentation and light weight. Online learning effectively solves the drawbacks of imbalance between supply and demand of offline education by solving the problems of time, distance, capacity, and scarcity of educational resources. In this paper, we take H International Education Group as the research subject. The paper combines the practice of precise English teaching theory and big data technology analysis tools to study the optimization of English teaching strategies in the K12 English training product market of H International Education Group. The materials prevalent in the English vocabulary market today are "one-size-fits-all," presenting the same vocabulary resources to all learners without taking into account the differences in learning styles, learning preferences, and original cognitive structures of different learners. [ABSTRACT FROM AUTHOR]

With the development of big data concept and technology, big data has an important impact on human development. This paper studies the relationship between the consumption pattern and mental health of enterprise employees under the normalization of epidemic prevention and control. Starting from the consumption structure and behavior of enterprise employees, it defines the meaning of enterprise employees' consumption and the connotation of enterprise employees' health psychology and analyzes the relationship between consumption behavior and consumption psychology and the elements of enterprise employees' health psychology. Based on the change of employees' income structure and consumption patterns, this paper speculates the relationship between employees' consumption patterns and mental health, analyzes the correlation between employees' consumption patterns and mental health through a questionnaire survey, and calculates the Correlation Clustering statistical results. It plays an important role in building a good enterprise staff consumption culture under the normalization of epidemic prevention and control and effectively realizes the significance of purifying the social consumption environment. [ABSTRACT FROM AUTHOR]

With the economic development, smart communities have been widely studied and applied. However, the system in this field is not perfect, and there are still a series of problems, such as high construction cost, low level of intelligence, mutual independence of different systems, difficulty in unified management, and so on. To solve the above problems, this paper proposes the smart community big data dynamic analysis model based on logistic regression model. First, this paper constructs the big data research architecture of smart community based on IOT technology, including IAAs, DAAS, PAAS, and SaaS layers and the virtual service layer of resource scheduling of spatiotemporal information cloud platform optimized by spatiotemporal law. And the IoT platform is designed to collect data to lay the foundation for research. Second, this paper is oriented to the big data application requirements with distribution and mobility as the main technical characteristics. Based on the distributed data flow, this paper designs mining operator to provide technical support for the data mining algorithm; at the same time, this paper constructs a high-dimensional random matrix model for measuring big data and then deduces its abnormal data detection theory and method to detect high-dimensional abnormal data. Finally, this paper uses logistic regression model to predict the development trend of smart community and provide guarantee for smart community service. The simulation results show the efficiency and accuracy of prediction can be improved based on logistic regression model. Furthermore, it effectively avoid repeated construction and waste of resources in the community and form a new community management model based on intelligent and information-based social management and service. [ABSTRACT FROM AUTHOR]

Due to the continuous progress of the era of mega data, it is more and more important to carry out accurate marketing of online video. Based on the analysis of users' big data, this paper proposes and implements an online video precision marketing algorithm, which provides a reference for the precision marketing strategy of video websites. This paper introduces the concept and characteristics of precision marketing and analyzes the application forms of precision marketing of video websites. It also summarizes the operation system, operation mode of precision marketing, and the influence of online video advertising on enterprises and users. In order to realize the accurate marketing of big data, it needs to be carried out from three parts: data collection, data analysis, and marketing plan formulation and implementation. Through continuously collecting customer data, it gradually and clearly acquires the characteristics of customers and constantly dynamically adjusts the marketing strategy to customers, so as to accurately conduct video marketing to customers and greatly improve the marketing efficiency. In order to test the effectiveness of the marketing algorithm in this paper, we conducted an experiment. The research in this paper has important practical value and reference significance for reducing the cost of video advertising of enterprises and improving the communication effect of online video advertising. [ABSTRACT FROM AUTHOR]

Under the current situation, there are many drawbacks in the integrated development of culture and tourism. Therefore, this paper aims to use big data analysis to study the path and development mechanism of cultural and tourism integration, aiming to explore a road of cultural and tourism integration that is most suitable for domestic development. This paper proposes a big data platform based on deep learning to adapt to domestic cultural data and tourism data. The system can intuitively analyze the domestic cultural tourism industry and display the development of cultural tourism with brief pictures and figures. This article analyzes the performance of the designed system and tests the fusion of cultural tourism data. The experimental results of this paper prove that the stability design of the big data cultural tourism industry analysis platform in this paper meets the requirements, and the platform can be stable at 5 Mb/s for 40–180 G big data transmission. The results of the modeling analysis of the integrated development of cultural tourism can be considered; that is, the cultural tourism industry has made great progress in the process of integration, and the integrated development of these two industries is imperative. [ABSTRACT FROM AUTHOR]

The stock market is a real and continuously evolving extremely complex dynamic system. This paper analyzes the change of stock market efficiency from the perspective of dynamic evolution, and the recursive graph method is used to obtain the dynamic characteristics of stock price time series. For the sharp rise and fall of stock prices, this paper uses the heuristic segmentation algorithm of nonlinear time series mutation detection to study the detection of market dynamics characteristics before the stock market crash. Based on the above research results, this paper studies the dynamic evolution of financial markets and the construction of a complex network of dynamic characteristics between financial markets. The simulation results show that there are typical characteristics of small world network in stock market complex network, the stock market complex network shows stronger synchronization ability, and the transmission range of information between the stock markets in the complex network is significantly expanded. [ABSTRACT FROM AUTHOR]

Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision-making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision-makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost. [ABSTRACT FROM AUTHOR]

Recently, the rapid development of experimental methods in the field of catalytic research allows for large amounts of data to be obtained. The use of new statistical and computational processing methods, including the extraction of information from experimental data and their unbiased interpretation, is important for accelerating the development and implementation of catalytic technologies. Necessary information can be extracted using statistical approaches such as PCA, MCR, and ALS. At the same time, machine learning algorithms are beginning to be actively used to interpret and build descriptive models. This paper discusses the main methods of machine learning and examples of their successful application to the analysis of infrared and X-ray absorption spectroscopic data. [ABSTRACT FROM AUTHOR]

In the era of big data, one of the important issues is how to recover the sets of true features when the data sets arrive sequentially. The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets. This is a type of online updating penalized likelihoods with differentiable or non-differentiable penalty functions. An online updating coordinate descent algorithm is proposed for solving the online updating optimization problem. Moreover, a tuning parameter selection is suggested in an online updating way. The selection and estimation consistencies and the oracle property are established, theoretically. Our methods are further examined and illustrated by various numerical examples from both simulation experiments and a real data analysis. [ABSTRACT FROM AUTHOR]

Nowadays, social data analysis is widely used in different businesses. Machine learning has proved its effectiveness in big data analysis for customers' segmentation and decision making. In the context of tourism and hospitality, there have been several studies for customers' decision making through social data analysis. However, the use of machine learning techniques for data-driven analysis by data obtained from social networking services is still in the early stage of development. This paper aims to develop a new hybrid method using machine learning techniques for social data analysis in hotel selection. The author develops the method by the combination of feature selection, supervised learning and unsupervised learning techniques. The author uses fuzzy k-means for data clustering, correlation-based feature selection for feature selection, and the ANFIS (adaptive neuro-fuzzy inference system) method for predicting customers' overall satisfaction in each cluster. The method is applied to a dataset for hotels in Saudi Arabia obtained from TripAdvisor. The prediction models of different clusters are evaluated using a set of evaluation metrics and compared with the other prediction techniques. The method was evaluated by mean-square error (MSE) and correlation coefficient (R2). The author compared the results of this study with other methods, which are ANFIS, support vector regression (SVR), artificial neural network (ANN) and decision trees. In this comparison, ANN was trained for 200 epochs and SVR was implemented by radial basis function kernel. The results revealed that the method that uses fuzzy k-means and neuro-fuzzy system (MSE = 0.0734; R2 = 0.9834) has better predicted overall ratings with a lower mean-squared error. [ABSTRACT FROM AUTHOR]

Big Data is a phenomenon that affects today's world, with new data being generated every second. Today's enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users' processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform's efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments. [ABSTRACT FROM AUTHOR]