Big Data Mining Research Articles

LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

ABSTRACT The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications.

Hsa_circ_0046430 promotes the progression of colorectal cancer by targeting miR-6785-5p/SRCIN1 axis as a ceRNA.

The correlation among circular RNAs (circRNAs), microRNAs, and messenger RNAs have gained increasing attention in recent years. However, the mechanism of such discoveries in colorectal cancer (CRC) is not yet elucidated. The present study aimed to clarify whether the novel circRNAs regulate the prognosis-related genes through the competing endogenous RNAs (ceRNA). An analysis of the Weighted Gene Co-Expression Network Analysis was conducted to screen a module-trait circRNAs, and other big data mining technologies were used to predict the related microRNAs and the downstream genes. Prognosis-related gene model was built using the Cox regression analysis for the 138 messenger RNAs associated with hsa circ 0046430. The qRT-PCR was adopted to verify ceRNA network. Immunohistochemistry verified the correlation between SRCIN1 and patient prognosis. In summary, these results demonstrated that hsa_circ_0046430 is a tumor-related circRNA based on the clinical characteristics module of Weighted Gene Co-Expression Network Analysis. The prognostic risk score signature model analysis indicated that CRC risk was independently related to the risk score and SRCIN1 was independently associated with overall survival. Therefore, the hsa_circ_0046430/miR-6785-5p/SRCIN1 axis was constructed. Hsa_circ_0046430/miR-6785-5p/SRCIN1 axis relative expression level was determined by qRT-PCR. Immunohistochemical staining further validated that SCRIN1 was significantly higher in cancer than in adjacent normal tissues. Our study identified and primarily validated the hsa_circ_0046430/miR-6785-5p/SRCIN1 regulatory axis impacted on CRC prognosis, suggesting novel biomarkers and therapeutic targets for CRC patients. Further in-depth studies are essential to confirm the underlying ceRNA mechanism.

Intelligent Intrusion Detection for Internet of Things Security: A Deep Convolutional Generative Adversarial Network-Enabled Approach

With the rapid advance of Internet of Things (IoT), it is difficult for cloud-centric computing to meet the requirements of low latency and ease of use. As an open and distributed system, edge computing integrates computing, networking, storage, and applications. It provides intelligent services on the edge of an IoT. The edge network is composed of various wireless and wired networks, and the computing and storage resources of edge nodes are limited. These conditions make the edge network expose to a variety of cyber attacks. Additionally, it is difficult for an IoT edge node to support large-scale network data collection and detection for IoT security. Although big data-enabled intrusion detection algorithms can ensure the high accuracy of intrusion detection systems, it is stressful for resource-limited edge nodes to implement those algorithms in IoT. Motivated by these challenges, we propose an intelligent intrusion detection algorithm implemented by big data mining based on a fuzzy rough set, generative adversarial network (GAN), and convolutional neural network (CNN). In our method, we first propose a fuzzy rough set-based algorithm to perform feature selection for big data via IoT. Then, we take advantage of the efficient feature extraction capabilities of CNN for implementing intrusion detection based on selected features. Furthermore, after combining CNN and GAN, we propose an intelligent algorithm to realize intrusion detection in a variety of scenarios. Finally, the proposed method is compared with existing methods for evaluation. Simulation results show that our method has up to 4% higher accuracy than existing methods.

AI for life: Trends in artificial intelligence for biotechnology

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.

Big data mining, rational modification, and ancestral sequence reconstruction inferred multiple xylose isomerases for biorefinery.

The isomerization of xylose to xylulose is considered the most promising approach to initiate xylose bioconversion. Here, phylogeny-guided big data mining, rational modification, and ancestral sequence reconstruction strategies were implemented to explore new active xylose isomerases (XIs) for Saccharomyces cerevisiae. Significantly, 13 new active XIs for S. cerevisiae were mined or artificially created. Moreover, the importance of the amino-terminal fragment for maintaining basic XI activity was demonstrated. With the mined XIs, four efficient xylose-utilizing S. cerevisiae were constructed and evolved, among which the strain S. cerevisiae CRD5HS contributed to ethanol titers as high as 85.95 and 94.76 g/liter from pretreated corn stover and corn cob, respectively, without detoxifying or washing pretreated biomass. Potential genetic targets obtained from adaptive laboratory evolution were further analyzed by sequencing the high-performance strains. The combined XI mining methods described here provide practical references for mining other scarce and valuable enzymes.

Bayes-based air pollution computer measurement and big data outlier detection model

In today’s rapidly changing social environment, high-dimensional data abound, but not all data are meaningful. In order to reduce costs and reduce consumption, people are more interested in some important variables. Therefore, variable selection of high-dimensional data is an important research direction. Based on the Bayesian method, it stands out among many variable selection methods with effective estimation efficiency and flexible mechanism, and it is also suitable for parametric and non-parametric models. Thanks to the flexibility of the statistical conclusion inference process, combined with prior information, the actual results are more accurate. At present, many indoor pollution problems, including decoration pollution, are getting more and more attention, and its air pollution calculations are obviously very important. Indoor air quality not only directly affects the comfort of the human body, but also significantly affects the work efficiency of family members. As a result of the development of the new digital economy, digital mining technology based on big data has brought unprecedented new development methods to various fields. This paper establishes an outlier detection model, identifies data processes that are significantly different from the normal data set or expected value, and cleans the data to improve the accuracy of the integrated detection model. This paper proposes a solution to this problem by combining big data mining technology with the establishment of an outlier detection model. At the same time, it optimizes the design of the model to reduce errors, so as to realize its accurate application in the measurement of air pollutants, and aims to promote its development.

Low Carbon Operation Mode of Large High Power Consumption Venues Based on Big Data Mining Technology

Given the high power consumption rate of large venues with high power consumption, excessive carbon emissions, and severe environmental pollution, this paper proposes a low-carbon operation mode for large venues with high power consumption based on big data mining technology. The DEA model of high-power consumption venues is constructed by linear programming. Then the preliminary indicators of low-carbon operation mode are selected based on big data mining technology. Finally, the selected indicators are used to set up the operation system. Through experimental analysis, it is found that the low-carbon operation mode of large venues with high power consumption can reduce 2 million kg of carbon emissions on the basis of the initial value of carbon emissions. This mode can effectively reduce the carbon emissions of high-power consumption venues and play a great role in curbing environmental pollution and improving the social benefits of venues.

The power of big data mining to improve the health care system in the United Arab Emirates

ContextCollecting and analyzing data has become crucial for many sectors, including the health care sector, where a hefty amount of data is generated daily. Over time, the amount and complexity of this data increase substantially. Consequently, it is considered big data that cannot be stored or analyzed conveniently unless advanced technologies are incorporated. Latest advances in technology have divulged new opportunities to use big data analysis to track a patient’s record and health. Still, it has also posed new challenges in maintaining data privacy and security in the healthcare sector.PurposeThis systematic review aims to give new researchers insights into big data use in health care systems and its issues or to advise academics interested in investigating the prospects and tackling the challenges of big data implementation in rising nations like the UAE. This study uses a systematic methodology to examine big data's role and efficacy in UAE health care.MethodsThe research follows the methodology of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the reviews and evaluating the randomized trials. Furthermore, the Critical Appraisal Checklist for PRISMA 2009 was applied for the research.FindingsThe study concludes that the healthcare systems in the United Arab Emirates can be improved through big data; however, the country authorities must acknowledge the development of efficient frameworks for performance, and quality assessment of the new health care system is significant. The said goal can be achieved via integrating big data and health informatics with the help of IT specialists, health care managers, and stakeholders. Data privacy, data storage, data structure, data ownership, and governance were the most often expressed concerns.Contribution to knowledgeBy discussing numerous issues and presenting solutions linked with big data, the current study contributes substantially to the knowledge of big data and its integration into health care systems in the UAE.

Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

The medical specialty of critical care, or intensive care, provides emergency medical care to patients suffering from life-threatening complications and injuries. The medical specialty is featured by the generation of a huge amount of high-granularity data in routine practice. Currently, these data are well archived in the hospital information system for the primary purpose of routine clinical practice. However, data scientists have noticed that in-depth mining of such big data may provide insights into the pathophysiology of underlying diseases and healthcare practices. There have been several openly accessible critical care databases being established, which have generated hundreds of scientific outputs published in scientific journals. However, such work is still in its infancy in China. China is a large country with a huge patient population, contributing to the generation of large healthcare databases in hospitals. In this data descriptor article, we report the establishment of an openly accessible critical care database generated from the hospital information system.

Cognitive Web Service-Based Learning Analytics in Education Systems Using Big Data Analytics

In the field of education, digital learning plays an important part. For each passing day, digital learning is displacing the traditional method of education. An accurate analysis of a student's qualities improves their academic performance. With the advancement of technology and big data, there are many applications for big data analytics, including education. Huge volumes of academic information are being generated, and discovering a technique to harness and analyze this information effectively is a challenging issue among many educational organizations. In this paper, educational clustering big data mining system (ECBDMS) has been proposed. The cognitive web service based learning analytic(CWS-LA) system is integrated to securely categorize and provide ease of access to the data. ECBDMS has been found to improve performance gains of 92.8%, prediction ratios of 88.6%, clustering error ratios of 2.3 percent, learning percentages of 94%, and forecasting accuracy of 97.1 percent when compared to other existing methods.

Research on unbalanced mining of highway project key data based on knowledge graph and cloud model

Various stages of highway project construction process involve text, image, audio, video and other related data sources involving many participants, forming a huge amount of data. Accurately tracing the source of responsibility, refining and applying the unbalanced data in the highway project archives is of great significance for realizing the intelligent transformation of highway construction project management. This paper firstly sorts out the construction process of highway projects and the main data sources, constructs a data association network between construction entities and construction process, as well as a knowledge map of highway construction data. Then, according to the highway construction stage, an index system based on 12 key data is constructed by using the entropy weight cloud model method, and the importance of the data is evaluated. Thirdly, based on the unbalanced characteristics of highway project data, a method of mining big data in highway project archives using classification evaluation indexes is proposed, and the accuracy of this method is verified by case calculation. Finally, taking the Shizong Qiubei Expressway in China as an example, the intelligent management and control suggestions for key data of transportation projects are proposed. It is found that the key data with special importance rate in highway construction include construction data, supervision data and completion data. Boosting algorithm is more accurate than the traditional SMOTE algorithm for unbalanced data mining, which helps to save the project construction cost and improve the quality of data extraction in the project archives. This study provides a theoretical reference for key data traceability of highway project intelligent management and control platform and the improvement of intelligent management efficiency.

Early Warning Model of College Students' Psychological Crises Based on Big Data Mining and SEM

In recent years, the psychological problems of college students could not be ignored, as they have seriously affected the growth of students and the normal teaching order of colleges and universities. However, there exists a strong noise in college students' psychological sample data set and a strong correlation between its data. Aiming to solve this problem, this paper proposes a psychological crisis warning method for college students based on big data mining and structural equation model (SEM). This method is oriented to massive user data in social networks. Particle swarm optimization is introduced to improve the random forest algorithm, and the original data is labeled to alleviate the impact of data noise on the recognition accuracy. The simulation example comes from an efficient actual data set in the southern China. The experimental results show that the proposed method can achieve an efficient analysis of actual complex data, and can provide reliable psychological auxiliary diagnosis for practitioners.

Business interest associations in the USA and Europe: evaluation of digital marketing techniques applied on their websites and social networks

Purpose This study aims to develop a proprietary indicator to measure the digital presence of the institutions: the digital presence index. Design/methodology/approach This research delves into how nonprofit institutions, specifically business interest associations (BIAs), have developed their internet presence by applying essential digital marketing techniques. To this end, and using big data mining tools, this study analyzes the tracking by internet users of 102 BIAs, with their respective websites in 36 countries in Europe and the USA. In addition, the presence and activity of the institutions included in this study on social networks are considered. Findings This research serves as a basis for discussing the current gap between social reality and the digitalization of institutions. In this sense, conclusions are drawn on the importance of managerial profiles in decision-making on digitization and the necessary knowledge that, together with Web and social network managers, they must have to articulate the means and techniques that promote the internet presence of the organizations they manage. Originality/value Conclusions are drawn according to the geographical scope of the BIAs, and an argument is made about the difficulties of connection and loss of prominence of this type of institutions among their different target audiences, especially among the youngest and most digitized.

High-frequency trading through artificial intelligence for financial innovation

High-frequency trading through artificial intelligence for financial innovation In the past two decades, Prof. Chien-Feng Huang has been working on several lines of interdisciplinary research across Artificial Intelligence (AI) and Finance, including high-frequency trading. His ultimate goal is to discover efficient as well as effective investment and trading models to create a blue ocean strategy in Finance. Big data technology, data mining and machine learning play key roles in his research because many niches have not been discovered yet or are not even comprehensible by humans currently. Prof. Huang regards this as an outstanding opportunity for AI to assist humans in exploring unknown territories in the investment world. Through AI, Prof. Huang thinks those who trust systematic, effective, reason-based investing strategies shall benefit from the systems he has developed.

Secure Medical Data Collection in the Internet of Medical Things Based on Local Differential Privacy

As big data and data mining technology advance, research on the collection and analysis of medical data on the internet of medical things (IoMT) has gained increasing attention. Medical institutions often collect users’ signs and symptoms from their devices for analysis. However, the process of data collection may pose a risk of privacy leakage without a trusted third party. To address this issue, we propose a medical data collection based on local differential privacy and Count Sketch (MDLDP). The algorithm first uses a random sampling technique to select only one symptom for perturbation by a single user. The perturbed data is then uploaded using Count Sketch. The third-party aggregates the user-submitted data to estimate the frequencies of the symptoms and the mean extent of their occurrence. This paper theoretically demonstrates that the designed algorithm satisfies local differential privacy and unbiased estimation. We also evaluated the algorithm experimentally with existing algorithms on a real medical dataset. The results show that the MDLDP algorithm has good utility for key-value type medical data collection statistics in the IoMT.

Research on the Spatial and Temporal Differences in Public Response to Release-Type Communication to Stop Food Waste

Release-type communication is a common way to guide the public to reduce food waste. It is of great significance to clarify the intervention effect of release-type communication on residents’ waste behavior and reveal the time evolution law of public response to reduce food waste. This study obtains 13,958 comments on food waste through big data mining and explores characteristics of public response to food waste behavior in different time and space from two dimensions of support intention and implementation intention. Through statistical analysis, empirical mode decomposition method, and cross-analysis, we find that: Public concern about release-type communication to stop food waste has experienced five stages: Incubation period, outbreak period, recession period, second outbreak period, and fading period. Overall, the support intention presents a down-up-down trend, the implementation intention rises and then descends, both appear inflection point in recession period. The trend term of empirical mode decomposition shows that the support intention goes down, the implementation intention goes up. Besides, the support intention and implementation intention of release-type communication to stop food waste are generally higher in western China. In particular, the support intention is significantly reduced in Beijing, Guangdong, and Fujian. The implementation intention is significantly increased in Shanghai, while it’s reduced notably in Shandong. This study provides important implications for guiding the public to reduce food waste, the government should formulate differentiated governance strategies to guide the public to reduce food waste according to the time change characteristics and spatial territorial differences of the public on release-type communication.

HKDC1 upregulation promotes glycolysis and disease progression, and confers chemoresistance onto gastric cancer.

There is increasing evidence that hexokinase is involved in cell proliferation and migration. However, the function of the hexokinase domain containing protein-1 (HKDC1) in gastric cancer (GC) remains unclear. Immunohistochemistry analysis and big data mining were used to evaluate the correlation between HKDC1 expression and clinical features in GC. In addition, the biological function and molecular mechanism of HKDC1 in GC were studied by in vitro and in vivo assays. Our study indicated that HKDC1 expression was upregulated in GC tissues compared with adjacent nontumor tissues. High expression of HKDC1 was associated with worse prognosis. Functional experiments demonstrated that HKDC1 upregulation promoted glycolysis, cell proliferation, and tumorigenesis. In addition, HKDC1 could enhance GC invasion and metastasis by inducing epithelial-mesenchymal transition (EMT). Abrogation of HKDC1 could effectively attenuate its oncogenic and metastatic function. Moreover, HKDC1 promoted GC proliferation and migration in vivo. HKDC1 overexpression conferred chemoresistance to cisplatin, oxaliplatin, and 5-fluorouracil (5-Fu) onto GC cells. Furthermore, nuclear factor kappa-B (NF-κB) inhibitor PS-341 could attenuate tumorigenesis, metastasis, and drug resistance ability induced by HKDC1 overexpression in GC cells. Our results highlight a critical role of HKDC1 in promoting glycolysis, tumorigenesis, and EMT of GC cells via activating the NF-κB pathway. In addition, HKDC1-mediated drug resistance was associated with DNA damage repair, which further activated NF-κB signaling. HKDC1 upregulation may be used as a potential indicator for choosing an effective chemotherapy regimen for GC patients undergoing chemotherapy.

Research on Precision Marketing Strategy of Commercial Consumer Products Based on Big Data Mining of Customer Consumption

Research on Precision Marketing Strategy of Commercial Consumer Products Based on Big Data Mining of Customer Consumption

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

ABSTRACT COVID-19 cripples the restaurant industry as a crucial socioeconomic sector that contributes immensely to the global economy. However, what the current literature less explored is to quantify the effect of COVID-19 on restaurant visitation and revenue at different spatial scales, as well as its relationship with the neighborhood characteristics of customers’ origins. Based on the Point of Interest (POI) measures derived from SafeGraph data providing mobility records of 45 million cell phone users in the US, our study takes Lower Manhattan, New York City, as the pilot study, and aims to examine 1) the change of restaurant visitations and revenue in the period prior to and after the COVID-19 outbreak, 2) the areas where restaurant customers live, and 3) the association between the neighborhood characteristics of these areas and lost customers. By doing so, we provide a geographic information system-based analytical framework integrating the big data mining, web crawling techniques, and spatial-economic modelling. Our analytical framework can be implemented to estimate the broader effect of COVID-19 on other industries and can be augmented in a financially monitoring manner in response to future pandemics or public emergencies.

Cluster analysis-based big data mining method of e-commerce consumer behaviour

In order to overcome the problems of low precision and long time of data mining in traditional big data mining methods of consumer behaviour, a clustering analysis method for big data mining of e-commerce consumer behaviour is proposed. In this paper, the K-means algorithm is used to calculate the similarity of behaviour clustering nodes of fee payers, determine the clustering process of consumer behaviour data and determine the mining weight of behaviour data. According to the FCM clustering algorithm, the target function for data mining of e-commerce consumer behaviour is constructed. According to Lagrange multiplication, the membership degree of consumer behaviour data is obtained, and the big data mining of consumer behaviour in e-commerce is realised. The experimental results show that with the method proposed in this paper, when the number of consumers is 500, the time for big data mining of consumer behaviour is 15.6s and the accuracy of behaviour big data mining is 95.34%.