Big Data Optimization Research Articles

AI-Enabled Multi-Mode Electronic Information Innovation Practice Teaching Reform Prediction and Exploration in Application-Oriented Universities

In view of professional learning and practical training in traditional electronic information education of application-oriented universities, this paper constructs electronic information–innovation practice teaching reform (EI-IPTR).In this scheme, by an integrating artificial intelligence (AI)-enabled curriculum with a multi-mode integrated platform and open-style module, big data-based comprehensive education resources are optimally configured. We jointly perform the multi-mode construction of innovative practice teaching, professional education stage design, and teaching management improvement, respectively. Subsequently, new practice teaching mechanisms with information technology and its implementation and management methods are established to achieve better teaching effects. It first strengthens learning and intra-group competition to promote students’ innovation in competitions. Then, the AI technique, i.e., attention mechanism-aided long short-term memory (LSTM), is used to model individual students’ abilities. Thus, it accurately evaluates them for teachers to efficiently manage their teaching process in accordance with their aptitude. The teaching reform practice verifies that the AI-enabled big data optimization of teaching reform has a better effect by the above multi-mode innovation. It exhibits an obvious improvement in the quantity and quality of students’ professional knowledge, personal ability, teamwork, and innovative practice. It is also in accordance with the independent completion of practical course teaching in the analysis of big education data. In addition, it realizes high-quality practical teaching by combining multi-mode, multi-level, and open discipline foundations together with efficient, professional skills.

Conceptual hybrid energy model for different power potential scales: Technical and economic approaches

This research attempts to address the gap between the theoretical fundamentals of hybrid renewable energy systems and their practical implementation at different scales through a new Conceptual Hybrid Energy Model (COHYBEM). The main objective was to develop a multi-variable model to allow a new complete and comprehensive techno-economic analysis of the performance of possible hybrid renewable power systems at different scales. The purpose is to evaluate the influence of critical parameters by changing key parameters in the developed model and identifying their impacts. It covers big data analyses, simulation and optimization of hybrid energy solutions, combining wind, solar and hydropower energy sources with the energy storage technology of pump hydropower storage. The research also denoted the Pareto front with the increasing power installed, for the maximum efficiency and total satisfied demand by Wind + PVSolar and by Hydro converges to a higher percentage, while a minimum waste by Wind + PVSolar is also progressing towards the increasing scales. In terms of investment costs for the 243 analyzed case studies, it varies between 45 k€ to 2.1 M€, resulting in a net present value (NPV) between 18 and 600 k€ and a payback period around 6–17 years depending on the power scale analyzed.

Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.

Internet of Things (IoT) applications and resources are highly vulnerable to flood attacks, including Distributed Denial of Service (DDoS) attacks. These attacks overwhelm the targeted device with numerous network packets, making its resources inaccessible to authorized users. Such attacks may comprise attack references, attack types, sub-categories, host information, malicious scripts, etc. These details assist security professionals in identifying weaknesses, tailoring defense measures, and responding rapidly to possible threats, thereby improving the overall security posture of IoT devices. Developing an intelligent Intrusion Detection System (IDS) is highly complex due to its numerous network features. This study presents an improved IDS for IoT security that employs multimodal big data representation and transfer learning. First, the Packet Capture (PCAP) files are crawled to retrieve the necessary attacks and bytes. Second, Spark-based big data optimization algorithms handle huge volumes of data. Second, a transfer learning approach such as word2vec retrieves semantically-based observed features. Third, an algorithm is developed to convert network bytes into images, and texture features are extracted by configuring an attention-based Residual Network (ResNet). Finally, the trained text and texture features are combined and used as multimodal features to classify various attacks. The proposed method is thoroughly evaluated on three widely used IoT-based datasets: CIC-IoT 2022, CIC-IoT 2023, and Edge-IIoT. The proposed method achieves excellent classification performance, with an accuracy of 98.2%. In addition, we present a game theory-based process to validate the proposed approach formally.

Big data research in the business, management and accounting field: Revealing the thematic structure based on co-word analysis

Objective. This study aims to describe the thematic structure of big data research in business, management, and accounting. Design/Methodology/Approach. A co-word analysis was performed on 12,345 documents retrieved from Scopus from 2014-2023 in the category “business, management, and accounting”. Modularity algorithms were used to identify themes and subthemes, and the clustering of terms was qualitatively analyzed. Results/Discussion. Five main themes were identified: (1) Business and economic data analysis, (2) big data analytics in marketing, consumer behavior, and employee dynamics, (3) scalable machine learning and big data optimization, smart cities and urban development, (4) IoT-Driven innovations in industry 4.0 for optimized supply chain management, and (5) Social media and sentiment analysis in modern tourism and hospitality. The grouping of terms demonstrated the broad application of big data in healthcare, education, tourism, industry, organizational development, finance, social media, marketing, and hospitality. Conclusion. Big data is a field of application. This is evident in each cluster, where there are sub-themes that are nothing more than applying big data principles in sectors such as manufacturing, tourism services, education, health, and urbanization. Generally, the findings here are similar to other studies that have analyzed broader or more selective literature.

A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization

Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics.

Research on Dynamic Optimization Algorithm of Warehousing Location Layout Based on Nonlinear Optimization

The paper aims to improve the turnover rate and operation efficiency of goods that are shipped out and replenished in the warehouses of electric power enterprises through big data analysis and optimization algorithms.The data is distributed in diverse locations and data nonlinear optimization algorithms certainly helps to understand the patterns for effective management of warehouses.This article focuses on reducing the delay in the operational processes.A multi-objective optimization (MOO) has been proposed which is aiming at improving the efficiency of transition process of commodities, storage, and overall warehouse operations.The study helps in the optimization of the allocation of cargo spaces with the aid of big data analysis optimization technology which collects and manages data in a distributed environment. A multi-objective cargo space optimization algorithm is proposed along with consideration of dynamic constraints.The algorithm is based on the coefficient of variation adaptive differential evolution algorithm.Individual decoding is performed according to the real-time cargo space availability.The simulation results show that the convergence speed of the algorithm is greatly improved.Meanwhile, the efficiency of warehouse transition process, shelf stability and the classification of commodities are remarkably improved.In nutshell, the multi-objective decision-making with the integration of big data analysis optimization technology assists in the effective organization of warehouse allocation system by considering multiple factors and constraints.

Towards enhanced heat and mass exchange in adsorption systems: The role of AutoML and fluidized bed innovations

The selection of optimal design and the most efficient operational parameters for energy devices constitute a priority task for sustainable development and increasing energy efficiency within the net-zero emissions strategy. This is particularly important in adsorption cooling and desalination systems with poor performance due to unfavourable heat transfer conditions in conventional packed beds of adsorption chillers (ACs). Therefore, looking for additional ways of performance improvement is still challenging, especially covering different design variants and operational strategies. The existing complex, time-consuming and costly analytical, numerical and experimental methods, usually focused on a specific design and operating parameters of conventional packed adsorption beds, cannot tackle these comprehensive problems. Since artificial intelligence (AI) based models are considered tools that sometimes may overcome the shortcomings of the programmed computing approach and the experimental procedures, the paper introduces automated machine learning (AutoML) as a general approach for the design and optimization study of adsorption cooling and desalination systems. The double-effect, i.e. specific cooling capacity (SCP) and specific daily water production (SDWP) of various adsorption chillers (ACs) operating in large-, pilot- and small-scale adsorption cooling and desalination systems, is considered in the study. The paper also presents a novel big data optimization procedure for selecting the best operating and design strategy in adsorption cooling and desalination technology. Finally, a new concept of fluidized bed-type application in adsorption chillers is proposed, which allows for enhancing the performance of ACs.The presented approach can be referred to as a complementary design technique in adsorption cooling and desalination systems, besides the existing complex analytical and time-consuming numerical methods and expensive experiments.

Analysis and Optimization of Learning Behavior of Music Students in Colleges and Universities Based on Big Data

Abstract As digital education continues to progress, more and more scholars are focusing on the analysis and optimization of big data in the field of education. However, the analysis and optimization of students’ learning behaviors using big data has received less attention. Therefore, this paper uses the improved K-means algorithm to cluster the four aspects of learning, diet, exercise, and consumption behaviors of music majors in College Z. We use the Apriori algorithm to conduct a correlation analysis between the clustered students’ consumption, life, learning, and grades. This analysis summarizes the characteristics of the students’ various behaviors and habits, enabling school administrators to provide effective and reasonable advice to the students. We used the improved K-means algorithm to identify five clustering results related to students’ behaviors. The correlation analysis revealed that 10.98% of the students were regular and hardworking, and there was a 97.78% probability that these students would get “excellent” grades. The majority of students who live a more regular life, spend more time on the Internet and have a low to medium level of consumption have a probability of getting “good” and “medium” grades, which indicates that the results of the big data survey are basically consistent with their actual situation. Obviously, the use of big data can improve the analysis of the correlation between students’ behaviors and grades.

Deep Learning Algorithm Research and Performance Optimization of Financial Treasury Big Data Monitoring Platform

Deep Learning Algorithm Research and Performance Optimization of Financial Treasury Big Data Monitoring Platform

Spatiotemporal Big Data Empower Community Modeling, Monitoring, Evaluation, and Optimization for Sustainable Community Development: A review of challenges and opportunities

Spatiotemporal Big Data Empower Community Modeling, Monitoring, Evaluation, and Optimization for Sustainable Community Development: A review of challenges and opportunities

Characterization of Red Civic Education in Colleges and Universities under Big Data Platform and Optimization of Teaching Methods

Abstract In the era of big data, the implementation of red ideological and political education has a positive and practical guide to the daily behavior habits of college students, and plays a positive guiding role. The lack of red ideological and political education is one of the main reasons for the abnormal behavior of college students. In this paper, the daily behavioral habits of college students are modeled, and according to the different distribution characteristics of individuals on campus activities, abnormal class determination and cluster analysis are carried out. The abnormality detection operator is used to detect the strange behavior of college students. In the study of abnormal behaviors of college students, the proportion of medium and low intensity of deviant behavior college students in sports behavior is as high as 73%, the proportion of high intensity in dietary behavior is 53%, and the proportion of high intensity in consumption behavior is also more than 50%. The psychological attention characteristic value of abnormal behavior college students is lower than that of average behavior college students, and the fluctuation is slightly more significant than that of intermediate behavior college students. Except for the forgiveness and modesty factors, the other 13 positive psychological quality factors of abnormal behavior college students are lower than those of average behavior college students, and the evaluation of the quality of political thinking is more inadequate than 80 points, which is in the range of poor and general assessment. Given the abnormal behavior of college students, the optimization direction of red Civics teaching method is proposed.

Research on Management and Optimization of Big Data Computing Engine Based on Cloud Native Technology IT Architecture

In the wave of digital transformation, we are in a data-driven era. With the explosive growth of data volume, traditional big data processing platforms are facing more and more challenges, including insufficient computational efficiency and resource utilization. At the same time, cloud native technology has emerged as a key factor in filling this gap due to its flexibility, automated management, and cross platform compatibility. Although cloud native technology has broad prospects, its application in the field of big data is still in its early stages, and there are still many problems that need to be solved. Among them, the unity of cluster management, the collaboration between operation and development, and the efficiency of technology architecture transformation are the main challenges currently faced. To address these issues, we will conduct in-depth research around cloud native technology, big data computing engine technology, and containerized services, exploring and implementing a big data computing engine management and optimization solution based on cloud native technology. By proposing and implementing cloud native deployment methods, containerized construction methods, and a one-stop big data application management and development platform, the aim is to provide new ideas and methods for solving many problems in traditional big data computing engine management, and to contribute to the development and innovation of the big data field.

Identification and Optimization of Big Data Mining Technology for Digital Economy Characteristics

Identification and Optimization of Big Data Mining Technology for Digital Economy Characteristics

Enhanced Coati Optimization Algorithm for Big Data Optimization Problem

Enhanced Coati Optimization Algorithm for Big Data Optimization Problem

Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

Chaotic golden ratio guided local search for big data optimization

Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition-based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC’2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.

Research on Big Data Resource Management and Optimization Based on Cloud Computing Technology

This paper points out the problems of irrational, complex and low utilization rate of massive data storage in most industries at present, analyzes the meaning and characteristics of big data, thus determining the necessity of distribution and management of big data, providing reference ideas for the management of Chinese big data resources, and further promoting the big data resources to play its due role. To provide support for the transformation of science and technology management decision-making, to achieve social and technological progress. On this basis, from different aspects of the mass data management methods and strategies and the significance and efficiency of these methods to achieve, improve the utilization rate of cluster resources, increase the ability of data sharing, a variety of computing frameworks can also share a distributed storage data, finally through the experiment demonstrated the effectiveness and security of the above strategies.

Big Data and Big Data Analytics in Value Creation and Innovation

Companies or organizations that want to continue to grow and develop in increasingly fierce competition need to innovate to create a competitive advantage. Big data has made it easier and more possible for organizations to make effective decisions thanks to the data-driven insights that big data analytics provides. Researchers and executive managers will focus more on data-driven decision-making and how to help businesses become data-driven by facilitating interactions between decision makers and the big data analytics pool. In this paper, we propose the role of innovation in driving the optimization of big data and big data analytics aimed at creating value for the company's progress.

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques.

The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling.

Big Data Optimization Analysis and Calculation of First Pass Rate of Special-shaped Curved Curtain Wall Keel Installation

The installation of special-shaped curved keel should not only be reliably connected with the main structure, but also meet the requirements of panel installation in the later stage of the curtain wall. Different professions have different construction precision, and there are inevitable errors in the construction process. Therefore, strict control of the first-pass rate of installation of special-shaped curved curtain keel is the key of curtain wall construction. In this paper, by making full use of BIM technology to optimize analysis and model program installation and construction, the first-pass rate of installation of special-shaped curved curtain wall keel is greatly increased from 76% to 92%. The successful application of this process provides reference for similar projects.