Big Data Mining Techniques Research Articles

Big data and predictive analytics: A systematic review of applications

Big data involves processing vast amounts of data using advanced techniques. Its potential is harnessed for predictive analytics, a sophisticated branch that anticipates unknown future events by discerning patterns observed in historical data. Various techniques obtained from modeling, data mining, statistics, artificial intelligence, and machine learning are employed to analyze available history to extract discriminative patterns for predictors. This study aims to analyze the main research approaches on Big Data Predictive Analytics (BDPA) based on very up-to-date published articles from 2014 to 2023. In this article, we fully concentrate on predictive analytics using big data mining techniques, where we perform a Systematic Literature Review (SLR) by reviewing 109 articles. Based on the application and content of current studies, we introduce taxonomy including seven major categories of industrial, e-commerce, smart healthcare, smart agriculture, smart city, Information and Communications Technologies (ICT), and weather. The benefits and weaknesses of each approach, potentially important changes, and open issues, in addition to future paths, are discussed. The compiled SLR not only extends on BDPA’s strengths, open issues, and future works but also detects the need for optimizing the insufficient metrics in big data applications, such as timeliness, accuracy, and scalability, which would enable organizations to apply big data to shift from retrospective analytics to prospective predictive if fulfilled.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

Quality Improvement Model of English Teaching in Universities Based on Big Data Mining

Big data mining in English teaching revolutionizes language instruction by leveraging vast amounts of data to personalize and optimize learning experiences. This approach utilizes sophisticated algorithms to analyze learners' language usage patterns, comprehension levels, and areas of difficulty. This paper presents a novel Quality Improvement Model of English Teaching (QIMET) tailored for universities, integrating big data mining techniques with a Stacked Hashing Edge Computing Model (SHECM). Recognizing the importance of enhancing English language proficiency among university students, particularly in an increasingly globalized educational landscape, this research aims to optimize English teaching methodologies through advanced computational approaches. The proposed QIMET framework leverages big data mining to analyze extensive datasets of student performance, language usage patterns, and instructional effectiveness. By extracting valuable insights from these datasets, educators can identify areas for improvement, tailor teaching materials, and personalize learning experiences to meet individual student needs. Simulation analysis of Across a dataset of 500 university students enrolled in English language courses, QIMET achieves an average improvement of 30% in language proficiency scores compared to traditional teaching methods. Moreover, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 25%, grammar comprehension by 20%, and communication skills by 35%. The integration of the SHECM enhances the computational efficiency and scalability of the QIMET framework. Real-time data processing and analysis enable educators to make timely interventions and adjustments to teaching strategies, resulting in more responsive and effective English language instruction.

Research on the death psychology among Chinese during and after the COVID-19 pandemic.

Under the threat of the novel coronavirus, people are compelled to contemplate some ultimate existential questions, such as life and death. This study collected texts related to the death psychology from Sina Weibo, and after data cleaning, a total of 3868 Weibo texts were included. Study 1 employed grounded theory from qualitative research to explore the core categories and evolutionary mechanisms of people's psychology when facing death threats in the context of the pandemic. Study 2 utilized big data mining techniques such as topic mining and semantic network analysis to validate the effectiveness of the death psychology theory developed in qualitative research. The findings demonstrate that within the "Emotion-Cognition-Behavior-Value" framework, the implications of death threats manifest in four aspects: death anxiety, death cognition, coping efficacy, and sense of meaning. As time progresses, the study of death psychology can be segmented into four distinct phases: the tranquil phase prior to lifting pandemic restrictions, the threat phase at lifting pandemic restrictions onset, the coping phase mid-lifting pandemic restrictions, and the reformative phase post-lifting pandemic restrictions. The calculated outcomes of topic mining and semantic network analysis corroborate the coding results and theories derived from the grounded theory. This reaffirms that data mining technology can be a potent tool for validating grounded theory.

A data‐driven assessment of mobile operator service quality in Ghana

AbstractThe rapid proliferation of mobile services has increased the need for data‐driven oversight of service quality, yet deriving insights from regulator‐collected datasets remains challenging. This study demonstrates techniques to tap the rich potential of drive test measurement data for analytical regulatory and policy decision‐making. Focusing on leading operator MTN in Ghana, we analyzed 4 years of drive test data supplied by the telecom regulator for the capital city of Accra. Three key performance indicators were evaluated—coverage, call setup time, and speech quality. We assessed service quality trends through statistical summaries, data visualization, and machine learning modeling and predicted speech quality scores. Our analysis revealed deteriorating performance post‐2019 and found that the light gradient boosting machine algorithm provided the highest accuracy predictions of speech quality. Overall, this work showcases how regulators can capitalize on vast datasets using big data mining techniques to evaluate network conditions over time and geography, enhancing field measurements for oversight. Our approach and techniques provide a template for evidence‐based policy‐making to uphold consumer service quality as mobile networks evolve.

Segmentasi Wilayah Indonesia Yang Berpotensi Budidaya Cabai Dengan Hierarki Clustering

Chili is one of the most widely consumed and highly price-volatile horticultural vegetables in Indonesia. In terms of consumption, it is a staple ingredient in the daily diet of most Indonesians. When chili prices surge, it not only affects the purchasing power of the population but also disrupts economic stability. Daily chili price data from the Department of Trade for the period of January 2010 to December 2015 demonstrates significant price fluctuations, primarily influenced by seasonal variations driven by weather conditions. Policy recommendations include promoting off-season chili cultivation, regulating production, and fostering farmer-industry partnerships. In pursuit of these objectives, mapping chili production regions in Indonesia using big data and data mining techniques, particularly hierarchical clustering, emerges as a valuable approach. This methodology allows the creation of models identifying optimal areas for chili cultivation, taking into account soil conditions conducive to optimal growth.The hierarchical clustering process employs average linkage and cosine distance to group regions based on climatic and environmental characteristics. The analysis reveals that Cluster C4, encompassing various provinces across Indonesia, exhibits the most favorable conditions for chili cultivation. This conclusion offers valuable guidance to farmers and researchers in determining the optimal locations for chili cultivation. Furthermore, the approach holds potential for application to other agricultural commodities, contributing to increased domestic production and price stability in the market.

Predicting daily precision improvement of Jakarta Islamic Index in Indonesia’s Islamic stock market using big data mining

PurposeThis paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.Design/methodology/approachThis research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.FindingsThe experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.Practical implicationsThis research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.Originality/valueThis research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

The influence of the Beijing Winter Olympic games on the demand for winter sports: An empirical analysis based on the Baidu Index

BackgroundThe 2022 Beijing Winter Olympics is a representative large-scale sporting event, which not only promotes the development of the national and regional economy, society, and culture but also increases the demand of residents for winter sports, thus helping achieve the grand goal of “encouraging 300 million people to participate in winter sports.” This research explores the influence of the Beijing Winter Olympics on residents’ demand for winter sports in the Beijing–Tianjin–Hebei and Yangtze River Delta urban agglomerations in China. MethodsApplying big data mining techniques, the Baidu Index of Winter Olympics-related terms are used to measure residents' interest in the Beijing Winter Olympics, and the ratio of the Baidu Index of five winter sports (ice skating, ice hockey, curling, luge, and skiing) to the number of internet searches is used to capture residents’ demand for winter sports. Moreover, we explore the spatial–temporal pattern of the interest in the Winter Olympics and the demand for winter sports and construct an econometric model to test the driving effect of the Winter Olympics empirically. ResultsThe results show that 1) since 2011, interest in Winter Olympics has been on the rise, and the interest of residents in Beijing–Tianjin–Hebei has been higher than that of the Yangtze River Delta; 2) the demand for skating and skiing, which are two popular winter sports, shows a declining geographical concentration, indicating that the popularity of these two sports is on the increase; 3) the demand for winter sports in the peripheral cities in Beijing–Tianjin–Hebei shows a trend of specialization, while Beijing, Tianjin, and some cities in the Yangtze River Delta present a trend of diversification; and 4) the interest in the Beijing Winter Olympics influences the demand for winter sports positively. ConclusionThis study shows that the increase in interest in the Beijing Winter Olympics boosts residents’ demand for winter sports, which implies that hosting Winter Olympics successfully drives winter sports participation in China.

Ecological Protection and Environmental Governance in the Era of Big Data Corporate Finance Political Performance Studies

The theory of sustainable development based on ecological protection and environmental governance places greater emphasis on the coordination between ecological civilization and economic construction. In response to the contradiction between economic development and ecological protection and environmental governance. We combine big data processing and big data mining techniques to conduct an in-depth study of ecological protection and environmental governance. In addition, we focus on exploring the relationship between relevant corporate financial performance and ecological protection and environmental governance. The results show that our proposed ecological conservation and environmental governance model has a maximum error of 2.37% and 1.27% in predicting ecological change and environmental governance respectively. The improvement in ecological conservation prediction is 63.72% and 65.93% respectively. In environmental governance, the improvement is 11.6% and 14.47%. The corresponding corporate earnings can be further increased by up to 37.81% and 41.36%. This shows that the adjustment of corporate finance can effectively solve the fluctuation of earnings caused by ecological protection and environmental management, and also promote the steady growth of corporate earnings.

Rapid screening of sensory attributes of mackerel using big data mining techniques and rapid sensory evaluation methods.

The present study aimed to investigate the potential of big data mining technology in conjunction with rapid sensory evaluation methods for the swift screening of sensory attributes of three kinds of frozen mackerel. Specifically, two rapid sensory evaluation methods, namely ideal profile method (IPM) and check-all-that-apply (CATA), were implemented and compared with the conventional descriptive analysis method. The results revealed that eight sensory attributes based on consumer network evaluations demonstrated significant consistency during the training process (p < .05). Notably, the application of web-based sensory attributes yielded highly comparable results between IPM and traditional descriptive analysis (0.915). Moreover, the results of the IPM preference map were in closer agreement with those of traditional descriptive analysis. While traditional sensory evaluation boasts high accuracy and a greater ability to detect nuances, the evolution of sensory evaluation technology has shifted its focus toward consumers. Rapid sensory evaluation analysis technology supports the collection of information directly from consumers, even by untrained or semi-trained groups, thereby presenting broad prospects for product qualitative analysis.

Artificial Intelligence and Machine Learning Algorithms for Assessing the Authenticity of a Scientific Article in Scopus: Translator's Experience

Objective. This paper examines ways to solve the problem of cross-language plagiarism in scientific works written in Ukrainian, which are to be translated and published in English. Considering that Ukrainian university libraries are directly involved in the practices of improving the level of awareness of lecturers and scientists, as well as their support of a large number of new digital tools, we draw attention to the emergence of new opportunities in the practices of supporting academic integrity. Methods. Big Data mining techniques and analysis of algorithms underlying machine translation software were employed to identify the cases of cross-language plagiarism in scientific articles originally written in the Ukrainian language. Results. Based on the analysis of 4000 translated manuscripts, it was established that the standard Microsoft Word 2022 software, typically used to write an article, identifies with a very high accuracy those parts of the text that had been earlier published and stored in a digital format. Conclusions. With the advent of Microsoft Office 365 software (released in 2022), it becomes possible to check any article originally written in Ukrainian or Russian, while being translated into English, for similarities with previously published academic papers. This allows for an instantaneous correction check that may prove useful in preventing the intended or unintended occurrence of cross-language plagiarism in scientific papers. It is advisable to more actively involve librarians of Ukrainian universities in using the powerful potential of digital support for the research activities of their users, including writing papers and checking them for signs of plagiarism.

Big data analytics to depict patient journey: demographics, calculation and the relative role of the CHA2DS2Vasc score components in patients diagnosed with atrial fibrillation in a tertiary hospital

Abstract Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer

Application of Exercise-Correlated Knowledge Proficiency Tracing Model based on Multiple Learning Objectives in English Teaching Curriculum Resources

The advanced invention of information technology overcomes the limitations of traditional teaching methods. The English-oriented online teaching system not only has rich course resources, but also provides technical support for students’ personalized learning. Therefore, using big data mining techniques may help to analyze students deeply and develop personalized study plans and content. Aiming at the deficiencies of online English teaching platform in the task of knowledge level diagnosis, an Exercise-correlated Knowledge Proficiency Tracing (EKPT) model that integrates multi-objective learning factors is proposed. The learning model maps students and practice information into meaningful knowledge space through practice and knowledge-related information, and quantifies students’ knowledge learning process through forgetting curve theory and learning curve theory. The experimental test results show that the EKPT model has the best performance in knowledge ability prediction and learning performance prediction. The mean absolute error (MAE) values of the EKPT model in the English1 dataset, English2 dataset, written auxiliary dataset and adaptive dataset are 0.32, 0.25, 0.39 and 0.34, respectively, with the smallest error. Therefore, the proposed EKPT model meets the requirements of English online learning. The research content has important reference value for the construction of online English teaching platform.

Big data analytics to depict patient journey: demographics, calculation and the relative role of the CHA2DS2Vasc score components in patients diagnosed with atrial fibrillation in a tertiary hospital

Abstract Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer

Quantifying production-living-ecology functions with spatial detail using big data fusion and mining approaches: A case study of a typical karst region in Southwest China

Demands for increasing production and living functions often threaten ecological functions. Reliable quantification of land use multifunctionality (LUMF) contributes to identify and coordinate these conflicts. However, the comprehensive LUMF indicator system and fine spatial resolutions restricted each other. Previous assessments were limited to coarse grids or administrative units, thus failing to provide detailed spatial information and causing scale biases, particularly in landscapes with strong heterogeneity. Combining big data mining and fusion techniques, this study built a comprehensive and quantitative evaluation scheme of production-living-ecology (PLE) land functions with spatial detail. The scheme was applied in the karst region of southwestern China as a case study. Data on socioeconomic status, remote sensing, and location points were fused using a variable-boundary spatialized approach to quantify human activity extent and degree of convenience. Specific environmental characteristics, such as topography and karst rocky desertification, were integrated into the spatialized PLE functions. The results showed that the global distribution of the production and living functions presented some similarity, yet the high production and living functions were not completely aligned. Also, the spatial patterns of the living and production functions usually showed opposite formations to the ecology function. Unlike the global relationship, the enlarged PLE relationships in the study regions validated the importance and necessity of fine spatial information. Local relationships between the production-living and production-ecology functions were significantly different in areas with various economic development levels, particularly in built-up areas. Regions with strong synergistic relationships of PLE functions were rare and were usually found in sightseeing areas of ecology and agriculture. These results demonstrated the effectiveness of our proposed method of spatializing PLE functions with spatial detail. The findings provide detailed spatial information on local anthropogenic and physical differences and support for balancing PLE functions and policy-making about land use optimization in practice.

The Application of Multi-Source Big Data Mining Techniques in the Analysis of Basketball Economic Management

In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China’s basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study.

Editorial on Special Issue “Geo-Information Technology and Its Applications”

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.

Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data

Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak.

Modeling and Simulation of Cell Signaling Networks for Subsequent Analytics Processes Using Big Data and Machine Learning.

This work explores how much the traditional approach to modeling and simulation of biological systems, specifically cell signaling networks, can be increased and improved by integrating big data, data mining, and machine learning techniques. Specifically, we first model, simulate, validate, and calibrate the behavior of the PI3K/AKT/mTOR cancer-related signaling pathway. Subsequently, once the behavior of the simulated signaling network matches the expected behavior, the capacity of the computational simulation is increased to grow data (data farming). First, we use big data techniques to extract, collect, filter, and store large volumes of data describing all the interactions among the simulated cell signaling system components over time. Afterward, we apply data mining and machine learning techniques—specifically, exploratory data analysis, feature selection techniques, and supervised neural network models—to the resulting biological dataset to obtain new inferences and knowledge about this biological system. The results showed how the traditional approach to the simulation of biological systems could be enhanced and improved by incorporating big data, data mining, and machine learning techniques, which significantly contributed to increasing the predictive power of the simulation.