Computer Big Data Research Articles

Linear Regression Analysis Results of the CGM Sensor PPG Comparison Between Predicted PPG Data Using the Linear Elastic Glucose Theory (LEGT) and Measured PPG Data During a ~2-year COVID-19 Quarantine Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 540)

Since 5/5/2018, the author has been applying a continuous glucose monitoring (CGM) sensor device on his upper arm that collected and recorded the complete glucose data continuously at 15-minute time intervals on his iPhone. He accumulated 96 glucoses per day over the past ~3.5 years. As a result, over these 1,272 days, he has compiled a total of 122,112 glucose data and stored them in his database where postprandial plasma glucose (PPG) occupies 45,792 data size and 37.5% of the total glucose database. During 2020-2021 COVID-19 quarantine period, he has a strictly managed routine, without any traveling, which allowed him to have an overall healthy lifestyle. Therefore, all of the 19 influential factors of PPG are mainly control by two primary factors: carbs/sugar intake amount (average at 13.1 gram, low-carb diet) and post-meal waking e excise (average of 4,300 steps). Based on this simplified and healthy lifestyle, he can then easily utilize his developed linear elastic glucose theory (LEGT) model to predict his PPG. In his previous research reports, he has applied physics concepts and theories, engineering models and equations, mathematical concepts and formulas, computer big data and artificial intelligence (AI) techniques, as well as some statistical approaches. The majority of published medical papers he has read are mainly based on statistics. As a result, in this article, he selected one of the basic statistical tools, linear regression analysis, to study the comparison between his predicted PPG using LEGT and CGM sensor measured PPG. In conclusion, the linear regression analysis results have provided similar findings with his previous analysis outcomes using other math-physical tools.

Linear Regression Analysis Results of the CGM Sensor PPG Comparison Between Predicted PPG Data Using the Candlestick Model and the LEGT Model Against the Measured Sensor PPG Data During a 2-year COVID-19 Quarantine Period for a Type 2 Diabetes Patient Based on GH-Method: MathPhysical Medicine (No. 541)

Since 5/5/2018, the author has been applying a continuous glucose monitoring (CGM) sensor device on his upper arm that collected and recorded the complete glucose data continuously at 15-minute time intervals on his iPhone. He accumulated 96 glucoses per day over the past ~3.5 years. As a result, over these 1,272 days, he has compiled a total of 122,112 glucose data and stored them in his database where postprandial plasma glucose (PPG) occupies 45,792 data size and 37.5% of the total glucose database. During the 2020-2021 COVID-19 quarantine period, he maintained a strict daily routine, without any travel, allowing him to reach an overall healthy lifestyle. Therefore, all of the 19 influential factors of PPG are mainly control by two primary factors: carbs/sugar intake amount (average at 13.1 gram, low-carb diet) and postmeal walking exercise (average of 4,300 steps). These lifestyle improvements helped reduce his PPG waveform amplitudes, including the four associated PPG data of candlestick (aka K-line) model: opening, maximum, minimum, and closing. Based on the simplified and healthy lifestyle, he can then easily utilize his developed candlestick (aka K-line) model to develop another set of predicted PPG values in addition to the LEGT model results shown in paper No. 540. In previous research reports, he applied physics concepts and theories, engineering models and equations, mathematical concepts and formulas, computer big data and artificial intelligence (AI) techniques, as well as some statistical approaches. However, the majority of published medical papers he read are mainly based on statistics. As a result, in this article, he selected one of the basic statistical tools, linear regression analysis, to study the comparison between his predicted PPG using K-line model and CGM sensor measured PPG. In conclusion, the linear regression analysis results using the K-line model provide more accurate predicted PPG results than using the LEGT model. Actually, both of the maximum PPG and minimum PPG of K-line model are generated through LEGT equation, but the starting PPG of K-line is a measured sensor value instead of the calculation via 0.97*FPG. Moreover, the K-line model contains one extra data point for the closing PPG value. This is why the K-line model has extracted from 4 out of 13 measured sensor PPG data which results in a higher prediction accuracy than the LEGT prediction calculated from carbs/sugar intake grams and post-meal walking steps.

Applying Multiple Regression Analyses and Linear Elastic Glucose Theory to Analyze and Compare the Predicted and Measured Finger-Pierced Postprandial Plasma Glucose using Carbs/Sugar Intake Amount and Post-Meal Walking Steps as Inputs over 2016- 2017 Period for a Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 544)

In the author’s previous research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selected some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted finger-piercing postprandial plasma glucose (PPG) as the output (dependent variable) by using his carbs/sugar intake grams and post-meal walking steps as inputs (independent variables). Since 1/1/2018, the author has been utilizing a finger-pierced device to collect and store his glucose data on the iPhone and Amazon cloud server. He has accumulated 4 glucose data per day over the past 10 years, along with entering his carbs/sugar intake grams and post-meal walking steps after each meal into the database. This article displays a multiple regression analysis result of the finger-piercing measured PPG data with the predicted finger PPG values (dependent output variables) by using his average daily carbs/sugar intake amounts and daily average post-meal walking steps (independent input variables) over a selected 2-year period from 1/1/2016 to 12/31/2017. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in any statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant. By utilizing his developed linear elastic glucose theory (LEGT), he calculates the predicted finger-piercing PPG using the same inputs of carbs/sugar and walking steps during the same chosen time period.

Applying Linear and Nonlinear Regression Analysis Models to Study the Possible Relationships between Cancer’s Risk and CVD/Stroke Risk over a 12-Year Period for a Type 2 Diabetes Patient Based on GH-Method: MathPhysical Medicine (No. 551)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and regression analyses, to study the predicted Cancer risk probability as the output (dependent variable) by using his MI-based CVD/Stroke risk as the input (independent variable). Since 1/1/2012, the author has been collecting various data related to his health (~3 million data) which includes 4 categories of medical conditions, obesity, diabetes, hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). Due to his limited knowledge in earlier years, the datasets from 2010 to 2012 are incomplete; therefore, the data used in this study for the initial period of 2010-2012 are his best-guessed data. Previously, he researched and published a few articles regarding the risks of having CVD/Stroke and Cancers based on his enhanced metabolism index (MI) model. In this particular paper, adopting a regression analysis model, he is able to compare the previously calculated Cancer risks based on the enhanced MI model versus the newly regression predicted Cancer risks using his MI-based CVD risks as input. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be < 0.05 to be considered as statistically significant. In summary, there are two specific conclusions worth mentioning: 1. The time-domain analysis results have revealed that the correlation between his Cancer risk and CVD risk are very high (89%). In addition, his enhanced MI-based model Cancer Risk and Regression Predicted Cancer Risk are highly correlated as well. 2. The space-domain linear regression analysis has shown that there is a variance of 1.0 existing between his Cancer risk and CVD risk. When using the nonlinear polynomial model, his variance still reaches 99.6%. This finding has further proven his Cancer risk and CVD risk are highly correlated together. Furthermore, he has conducted two more linear regression analyses of Cancer risk vs. Lifestyle and CVD risk vs. Lifestyle which reveal two extremely high linear variances at 1.0. These findings offer additional indication that Lifestyle, the common root-cause, contributes highly on both CVD risk and Cancer risk.

Applying Multiple Regression Analyses to Compare the Regression Predicted and Measured Body Weight Using Food Quantity and Sleep Score as Inputs over a 6.5-Year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 547)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted body weight as the output (dependent variable) by using his foods quantity and sleep score as inputs (independent variables). Since 5/1/2015, the author has been collecting various data related to his food nutrition (~0.5 million data) and sleep conditions. The Food Details (FD) category includes both food quantity (m9a) and food quality (nutrition, m9b). Previously, he has researched and published a few articles regarding the relationship between body weight and quantity of food consumption. During 11/8/2020 - 11/10/2021, he experimented with intermittent fasting experiments to study their inter-connectivity. In this paper, he will combine sleep score (m7) with food quantity (m9a) and connected them with his body weight in early morning (m1) using the multiple regression analysis method. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant.

Applying Multiple Regression Analyses to Compare the Regression Predicted and Originally Calculated CVD/Stroke Risk Probabilities Using Medical Condition and Lifestyle Detail Scores as Inputs over a 10-year Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 546)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selects some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted CVD/Stroke risk probability as the output (dependent variable) by using his medical condition and lifestyle detail scores as inputs (independent variables). Since 1/1/2012, the author has been collecting various data related to his health (~3 million data) which includes 4 categories of medical conditions, obesity, diabetes, hypertension, and hyperlipidemia (m1 through m4), along with 6 categories of lifestyle details, including exercise, water intake, sleep, stress, food, and daily life routines (m6 through m10). However, due to the limitation of his learned knowledge in earlier years, the data from 2012-2013 is incomplete; therefore, the scores in this study for the initial period of 2012-2013 are his best-guessed data based on an incomplete dataset. Previously, he has researched and published a few articles regarding the risks of having CVD/Stroke based on his metabolism index (MI). In this paper, he will compare his previously calculated CVD risks based on the MI method versus the newly calculated CVD risks based on multiple regression analysis methods. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant.

Exploring and validating different relationships among various biomarkers by using both linear and nonlinear, single variable and multiple variables regression analysis models and collected big data of a type 2 diabetes patient based on GH-Method: math-physical medicine (No. 549)

In the author’s previous medical research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches to explore and interpret various biophysical phenomena. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, for his paper no. 540 through no. 548 (except no. 545), he has selected some basic statistical tools, such as correlation, variance, p-values, and regression analyses, to study his various biomarkers using linear regression analysis model with either single variable or multiple variables. In this particular paper, he has selected 5 cases to compare their concluding findings using both linear regression and nonlinear regression. The nonlinear regression models include exponential, logarithmic, polynomial, and power. The biomarkers he selected to study are body weight for obesity, FPG & PPG for diabetes, and CVD/Stroke risk probability (chronic disease complication). The inputs or independent variables are carbohydrates & sugar intake amount, body weight, 4 medical conditions score, 6 lifestyle details score, sleep, food consumption quantity, and HbA1C to indicate the insulin resistance level. Depending on his selected case, the body weight has served as either output of dependent variable (symptom) or input independent variable (cause). By the way, since 1/1/2012, the author has collected ~3 million data thus far regarding his health, lifestyle, organs, and diseases. His 5 selected cases are listed as below: 1. CGM sensor PPG vs. carbs/sugar 2. CGM sensor FPG vs. weight 3. CVD risk vs. disease & lifestyle 4. Weight vs. sleep & food quantity 5. Finger FPG vs. sleep, weight, A1C. The analyses associated with these 5 selected cases utilized two different approaches. Time-domain analysis uses time as the x-axis and biomarker as the y-axis, whereas the space-domain analysis uses the independent x variable as the x-axis and the dependent y variable as the y-axis. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in many statistics textbook. It should be noted that, in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity. The variance, R^2, should be the higher (covers more data) the better. The p-value should be <0.05 or 5% to be considered as statistically significant.

Applying Multiple Regression Analysis and Linear Elastic Glucose Theory to Analyze and Compare the Predicted Postprandial Plasma Glucose Using Carbs/Sugar Intake Amount and Post-Meal Walking Steps as Inputs over an Approximate 2-Year COVID-19 Quarantine Period from a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 543)

In the author’s previous research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches. The majority of medical research scientists’ published papers he has read thus far are primarily based on statistics. As a result, in this article, he selected some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses to study the predicted postprandial plasma glucose (PPG) as the dependent variable using his carbs/sugar intake grams and post-meal walking steps as inputs (independent variables). Since 5/8/2018, the author has been utilizing a continuous glucose monitoring (CGM) sensor device on his upper arm that collected and recorded the complete glucose data continuously at 15-minute time intervals on his iPhone. He accumulated 96 glucoses per day over the past ~3.5 years. After each meal, he collects 13 PPG data, accumulating 39 PPG values per day, along with entering his carbs/sugar intake grams and post-meal walking steps. This article displays multiple regression analysis results of measured PPG values with predicted PPG values (dependent outputs) by using his average daily carbs/sugar intake amounts and daily average post-meal walking steps (independent inputs) over an approximate 2-year COVID-19 quarantine period from 1/1/2020 to 10/31/2021. In this study, he will not repeat the detailed introduction of regression analysis in the Method section because it is available in any statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5. to indicate strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant. By utilizing his developed linear elastic glucose theory (LEGT), he calculates the predicted PPG using the same inputs of carbs/sugar and walking steps during the same chosen time period.

Applying Multiple Regression Analyses and Linear Elastic Glucose Theory to Analyze and Compare the Predicted and Measured Finger-Pierced Postprandial Plasma Glucose using Carbs/Sugar Intake Amount and Post-Meal Walking Steps as Inputs over 2016- 2017 Period for a Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 544)

In the author’s previous research reports, he mainly applied physics theories, engineering models, mathematical equations, computer big data analytics and artificial intelligence (AI) techniques, as well as some statistical approaches. However, the majority of medical research papers he has read thus far are primarily based on statistics. As a result, in this article, he selected some basic statistical tools, such as correlation, variance, p-values, and multiple regression analyses, to study the predicted finger-piercing postprandial plasma glucose (PPG) as the output (dependent variable) by using his carbs/sugar intake grams and post-meal walking steps as inputs (independent variables). Since 1/1/2018, the author has been utilizing a finger-pierced device to collect and store his glucose data on the iPhone and Amazon cloud server. He has accumulated 4 glucose data per day over the past 10 years, along with entering his carbs/sugar intake grams and post-meal walking steps after each meal into the database. This article displays a multiple regression analysis result of the finger-piercing measured PPG data with the predicted finger PPG values (dependent output variables) by using his average daily carbs/sugar intake amounts and daily average post-meal walking steps (independent input variables) over a selected 2-year period from 1/1/2016 to 12/31/2017. In this study, he will not repeat the detailed introduction of the regression analysis in the Method section because it is available in any statistics textbook. It should be noted that in regression analysis, the correlation coefficient R should be > 0.5 or 50% to indicate a strong inter-connectivity and the p-value should be <0.05 to be considered as statistically significant. By utilizing his developed linear elastic glucose theory (LEGT), he calculates the predicted finger-piercing PPG using the same inputs of carbs/sugar and walking steps during the same chosen time period.

Online Classroom Teaching Quality Evaluation System Based on Facial Feature Recognition

With the rapid development of computer big data technology, online education in the form of online courses is increasingly becoming an important means of education. In order to objectively evaluate the teaching quality of online classroom, a teaching quality evaluation system based on facial feature recognition is proposed. The improved (MTCNN) multitask convolutional neural network is used to determine the face region, and then the eye and mouth regions are located according to the facial proportion relationship of the face. The light AlexNet classification based on Ghost module was used to detect the open and close state of eyes and mouth and combined with PERCLOS (percentage of eye closure) index values to achieve fatigue detection. Large range pose estimation from pitch, yaw, and roll angles can be achieved by easily locating facial feature angles. Finally, the fuzzy comprehensive evaluation method is used to evaluate students’ learning concentration. The simulation experiments are conducted, and the results show that the proposed system can objectively evaluate the teaching quality of online courses according to students' facial feature recognition.

The Spatial Form of Digital Nonlinear Landscape Architecture Design Based on Computer Big Data

Abstract Condensing the multi-dimensional digital model of green urban design, and constructing a digital method system of it progressively layer by layer. Based on this research background, the dissertation designs the spatial form of landscape architecture based on the data visualisation of nonlinear technology. The article uses the colour zoning method to design the actual scene of the garden landscape with nonlinear parameteriszation. The simulation result analyses that the proposed nonlinear algorithm has realised the efficiency improvement purpose of landscape architecture design.

Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

Efficiency Analysis of OLAP-data Hypercube Decomposition for Exponential Computational Complexity Methods

The paper studies problems of reduction (decomposition) of OLAP-hypercube multidimensional data models. When decomposing large hyper-cubes of multidimensional data into sub-cube components the goal is to increase the computational performance of analytical OLAP systems, which is related to decreasing computational complexity of reduction methods for solving OLAP-data analysis problems with respect to the computational complexity of non-reduction methods, applied to data directly all over the hypercube. The paper formalizes the concepts of reduction and non-reduction methods and gives a definition of the upper bound for the change in the computational complexity of reduction methods in the decomposition of the problem of analyzing multidimensional OLAP-data in comparison with non-reduction methods in the class of exponential degree of computational complexity.The exact values of the upper bound for changing computational complexity are obtained for the hypercube decomposition into two sub-cubes on sets consisting of an even and an odd number of sub-cube structures, and its main properties are given, which are used to determine the decomposition efficiency. A formula for the efficiency of decomposition into two sub-cube structures for reduction of OLAP data analysis problems is obtained, and it is shown that with an increase in the dimension “n” of the lattice specifying the number of sub-cubes in the hypercube data structure, the efficiency of such a decomposition obeys an exponential law with an exponent “n/2”, regardless of the parity “n”. The examples show the possibility to use the values (found) of the upper bound for the change in computational complexity to establish the effectiveness criteria for reduction methods and the expediency of decomposition in specific cases.The paper results can be used in processing and analysis of information arrays of hypercube structures of analytical OLAP systems belonging to the Big-Data or super-large computer systems of multidimensional data.

Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.

The big data revolution presents an exciting frontier to expand public health research, broadening the scope of research and increasing the precision of answers. Despite these advances, scientists must be vigilant against also advancing potential harms toward marginalized communities. In this review, we provide examples in which big data applications have (unintentionally) perpetuated discriminatory practices, while also highlighting opportunities for big data applications to advance equity in public health. Here, big data is framed in the context of the five Vs (volume, velocity, veracity, variety, and value), and we propose a sixth V, virtuosity, which incorporates equity and justice frameworks. Analytic approaches to improving equity are presented using social computational big data, fairness in machine learning algorithms, medical claims data, and data augmentation as illustrations. Throughout, we emphasize the biasing influence of data absenteeism and positionality and conclude with recommendations for incorporating an equity lens into big data research.

How does the 'environment' come to the person? The 'ecology of the person' and addiction.

Currently, psychiatry lacks a field that can be called “theoretical psychiatry”, which uses theoretical concepts and explanatory models: The main stream of research is to collect data of all kinds in the hope that the computational Big Data approach will shed a bright light on the black box of mental disorders. Accordingly, the biology-based Research Domain Criteria of the National Institute of Mental Health have been established. However, as philosophical analyses of concepts and methods have shown, several epistemological gaps stand in the way of a consistent multilevel understanding of mental disorders. Also, the implicit ontological problems in the biological reduction of the psychosocial level and in the integration of so-called hard and soft disciplines are mostly left out. As a consequence, a non-reductive psychological theory of mental disorders is sought that also integrates correlating biological and sociological issues. In this context, one example of promising nonreductive psychiatric research is the option of systems/network psychopathology. The possibilities for integrating different psychological perspectives are highlighted for the field of addiction research and treatment, where pragmatic behaviorist approaches dominate over the theory-based practice of psychoanalysis. In comparing the theoretical constructs of these two approaches, the relevance of the concept of “(social) environment” as the wealth of influential sociocultural factors is discussed at levels superior to the interpersonal micro-level, namely the organizational meso- and societal macro level, which is not sufficiently considered in current biopsychiatry. On this basis of argumentation, the usefulness of grounding and framing psychiatry through the field of ecological sciences, especially human ecology, is demonstrated. Finally, to this end, an outline of an ecological model of mental health and illness is presented.

Computer Big Data Technology in Internet Network Communication Video Monitoring of Coal Preparation Plant

The paper uses the control technology of computer big data and PLC, fieldbus communication technology and video monitoring technology to research, design and develop the monitoring system of the coal preparation plant’s production process. In this plan, the coal preparation plant’s video monitoring system, production centralized control system and other production support systems are fully integrated through network technology, to achieve the purpose of improving its safety guarantee function. The system improves the level of visual management, realizes unattended operation, and reduces potential safety hazards.

Study and application of computer information big data in basketball vision system using high-definition camera motion data capture

Contemporary computer big data technology is developing rapidly, and it has played a role in promoting the intelligent development of sports. Based on this research background, the thesis revolves around the application of computer big data artificial intelligence in the field of basketball training. The study found that computerized big data in basketball training and teaching is mainly reflected in the following aspects: high-definition camera motion data capture, player analysis to obtain assisted training system, etc. The final paper uses a basketball vision system as a case study to analyze specific applications of big data for computer information.

Research on Dynamic Knowledge Map Service System Using Computer Big Data

The emergence of computer big data related data provides a new method for the construction of knowledge links in the knowledge map. This realizes an objective knowledge network with practical significance that is easier to be understood by machines. The article combines the four principles of linked data publishing content objects and their semantic characteristics, and uses the RDF data model to convert unstructured data on the Internet and structured data that adopts different standards into unified standard structured data for association. The system forms a huge knowledge map with semantics, intelligence, and dynamics.

Application of Cloud Computing Technology in Computer Big Data Analysis

As a new technology of data processing and information analysis, cloud computing technology can process and analyze information and data through the use of multiple servers, so as to provide users with accurate information and data. Strengthening the application research of cloud computing technology in big data analysis can not only improve the processing efficiency of information and data, but also create conditions for the optimization and improvement of related technologies. Combined with the actual development of cloud computing technology in China, this paper analyzes the practical application of this technology in computer big data analysis.

Frequency Converter as a Node for Edge Computing of Big Data, Related to Drive Efficiency, in Industrial Internet of Things

The article presents a method of generating key performance indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in a frequency converter. The authors proved that using the proposed solution, it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage flux control without encoder, vector voltage flux control with encoder, vector current flux control, and vector current flux control with torque control). During the experiments, values of the current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque, and the current rotational speed were measured and processed. Based on the data, the level of energy efficiency was determined for various control modes.