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The Communist Party of China's 19th National Congress Report has clearly set the goal of "smart society" and put forward new requirements for the current development of territorial spatial planning. Nowadays, the empowerment of information technology based on the Internet, big data, cloud computing, etc. is the main driving force and development focus of the current practices of smart territorial spatial planning. How to understand and promote the development of smart society is the foundation of the compilation and implementation of smart territorial spatial planning. This paper emphasizes the importance of human-land relationship and the theory of urban life organism to the planning and governance of territory in a smart society. It points out that the overall conceptualization of smart territorial spatial planning should be transformed from informational empowerment to comprehensive empowerment, which includes technological empowerment and innovative empowerment. The paper constructs a smart territorial spatial planning framework-EPTI-based on the ideas of ecological civilization, people- oriented, technology integration application and institutional innovation, and discusses the paths toward smart compilation and implementation of territorial spatial planning. [ABSTRACT FROM AUTHOR]

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Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Serverless computing has gained significant popularity due to its scalability, cost-effectiveness, and ease of deployment. With the exponential growth of data, organizations face the challenge of efficiently processing and analyzing vast amounts of data in a serverless environment. Data pipelines play a crucial role in managing and transforming data within serverless architectures. This paper provides a taxonomy of data pipeline approaches in serverless computing. Classification is based on architectural features, data processing techniques, and workflow orchestration mechanisms, these approaches are categorized into three primary methods: heuristic-based approach, Machine learning-based approach, and framework-based approach. Furthermore, a systematic review of existing data pipeline frameworks and tools is provided, encompassing their strengths, limitations, and real-world use cases. The advantages and disadvantages of each approach, also the challenges and performance metrics that influence their effectuality have been examined. Every data pipeline approach has certain advantages and disadvantages, whether it is framework-based, heuristic-based, or machine learning-based. Each approach is suitable for specific use cases. Hence, it is crucial assess the trade-offs between complexity, performance, cost, and scalability, while selecting a data pipeline approach. In the end, the paper highlights a number of open issues and future investigations directions for data pipeline in the serverless computing, which involve scalability, fault tolerance, data real time processing, data workflow orchestration, function state management with performance and cost in the serverless computing environments. [ABSTRACT FROM AUTHOR]

In the past few years, the field of big data has multiplied, with more academic papers written about it. This bibliometric research was done to look at and understand the trends regarding countries, organizations, authors, and keywords that are creating the most publications and citations in big data. This study was done to understand the current state of scientific publications in the field. The research used Web of Science (WoS) database information from 1993 to 2021. The study of 32,085 papers showed that, on average, each document has 14.7 citations and 3.46 citations per year. According to the results, the United States, China, and the United Kingdom have the most scholarly publications about big data. The Chinese Academy of Science, Harvard University, and Stanford University were the three most productive groups. When researching big data, most writers work together, and most terms are related to big data analytics, machine learning, and cloud computing. [ABSTRACT FROM AUTHOR]

Over the last years, cloud computing has emerged as a natural choice to host, manage, and provide various kinds of virtualized resources (e.g., software, business processes, databases, platforms, mobile and social applications, etc.) as on-demand services. This "servicelization" across various domains has produced a huge volume of data, leading to the emergence of a new service model, called big service. This latter consists of the encapsulation, abstraction and the processing of big data, allowing then to hide their complexity. However, this promising approach still lacks management facilities and tools. Indeed, due to the highly dynamic and uncertain nature of their hosting cloud environments, big services together with their accessed data need continuous management operations, so that to maintain a moderate state and high quality of their execution. In this context, frameworks for designing, composing, executing and managing big services become a major need. The purpose of this paper is to provide an understanding of the new emerging big service model from the lifecycle management phases' point of view. We also study the role of big data frameworks and multi-cloud strategies in the provisioning of big services. A research road map on this topic will be summarized at the end of this paper. [ABSTRACT FROM AUTHOR]

This paper explores the fundamental elements of cloud computing architecture, discussing the integration of cloud computing with Big Data, the use of homomorphic encryption, and remote sensing data in cloud architecture. It also examines advanced cloud architectures, such as Hypervisor Clustering architecture, Load Balanced Virtual Server Instances architecture, Zero Downtime architecture, and Resource Reservation architecture, emphasizing their role in enhancing availability and scalability. The paper concludes by showcasing the significance of cloud computing architecture in addressing modern computational and data management challenges. [ABSTRACT FROM AUTHOR]

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, the size and complexity of data continue to increase, which makes the requirement for data privacy and security increasingly urgent. However, traditional encryption methods cannot meet the demand for efficient searching in large-scale datasets. To solve this problem and enable users to search within encryped data and without decrypting the entire dataset, trapdoor functions and other cryptograhic techniques are introduced in searchable encryption. However, searchable encryption still cannot meet the needs in the real world. Therefore, researchers have introduced the concept of attribute-based encryption into searchable encryption, resulting in attribute-based searchable encryption (ABSE). This approach aims to achieve efficient search by attributes in encrypted datasets. ABSE has a wide range of applications in the fields of privacy protection, data sharing, and cloud computing. In this paper, we describe the trends in development, focusing on enhancing security, improving computational efficiency, and increasing flexibility. We also present the related schemes. In addition, several common application areas are introduced and the relevant schemes proposed by researchers are summarized. Moreover, the challenges and future directions of ABSE are discussed in this paper. [ABSTRACT FROM AUTHOR]

Operating System (OS) is a critical piece of software that manages a computer's hardware and resources, acting as the intermediary between the computer and the user. The existing OS is not designed for Big Data and Cloud Computing, resulting in data processing and management inefficiency. This paper proposes a simplified and improved kernel on an x86 system designed for Big Data and Cloud Computing purposes. The proposed algorithm utilizes the performance benefits from the improved Input/Output (I/O) performance. The performance engineering runs the data-oriented design on traditional data management to improve data processing speed by reducing memory access overheads in conventional data management. The OS incorporates a data-oriented design to "modernize" various Data Science and management aspects. The resulting OS contains a basic input/output system (BIOS) bootloader that boots into Intel 32-bit protected mode, a text display terminal, 4 GB paging memory, 4096 heap block size, a Hard Disk Drive (HDD) I/O Advanced Technology Attachment (ATA) driver and more. There are also I/O scheduling algorithm prototypes that demonstrate how a simple Sweeping algorithm is superior to more conventionally known I/O scheduling algorithms. A MapReduce prototype is implemented using Message Passing Interface (MPI) for big data purposes. An attempt was made to optimize binary search using modern performance engineering and data-oriented design. [ABSTRACT FROM AUTHOR]

Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today's data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0. [ABSTRACT FROM AUTHOR]

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

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy. [ABSTRACT FROM AUTHOR]

Huge data over the cloud computing and big data are processed over the network. The data may be stored, send, altered and communicated over the network between the source and destination. Once data send by source to destination, before reaching the destination data may be attacked by any intruders over the network. The network has numerous routers and devices to connect to internet. Intruders may attack any were in the network and breaks the original data, secrets. Detection of attack in the network became interesting task for many researchers. There are many intrusion detection feature selection algorithm has been suggested which lags on performance and accuracy. In our article we propose new IDS feature selection algorithm with higher accuracy and performance in detecting the intruders. The combination of wrapper filtering method using Pearson correlation with recursion function is used to eliminate the unwanted features. This feature extraction process clearly extracts the attacked data. Then the deep neural network is used for detecting intruders attack over the data in the network. This hybrid machine learning algorithm in feature extraction process helps to find attacked information using recursive function. Performance of proposed method is compared with existing solution. The traditional feature selection in IDS such as differential equation (DE), Gain ratio (GR), symmetrical uncertainty (SU) and artificial bee colony (ABC) has less accuracy than proposed PCRFE. The experimented results are shown that our proposed PCRFE-CDNN gives 99% of accuracy in IDS feature selection process and 98% in sensitivity. [ABSTRACT FROM AUTHOR]

Massive multimedia data are being created due to the rising amount of the Internet and user-generated content, low-cost commodity devices with cameras (like cellphones, surveillance systems, and so on), and the proliferation of social networks, forming a unique type of big data. Several studies have been conducted in this research area using a survey and event analysis approach; however, none has been conducted to investigate the status of knowledge, its features, evolution, and emerging trend of multimedia big data. Therefore, in this paper, a bibliometric study using VOSviewer software is carried out with 1,865 documents from 2008 to 2020. Based on the result, 2013 is the starting year where the total publication excess of 100 articles and the configuration of leading countries, productive organizations, and authors are investigated. The most cited journals, popular publications venues, and hot research topics are also included in the investigations. Our investigation uncovered useful information, such as annual publishing patterns, the hottest research topic, the top 10 important authors and articles, and the most helpful funding organizations and venues. [ABSTRACT FROM AUTHOR]

Large amounts of data are created from sensors in Internet of Things (IoT) services and applications. These data create a challenge in directing these data to the cloud, which needs extreme network bandwidth. Fog computing appears as a modern solution to overcome these challenges, where it can expand the cloud computing model to the boundary of the network, consequently adding a new class of services and applications with high-speed responses compared to the cloud. Cloud and fog computing propose huge amounts of resources for their clients and devices, especially in IoT environments. However, inactive resources and large number of applications and servers in cloud and fog computing data centers waste a huge amount of electricity. This paper will propose a Dynamic Power Provisioning (DPP) system in fog data centers, which consists of a multi-agent system that manages the power consumption for the fog resources in local data centers. The suggested DPP system will be tested by using the CloudSim and iFogsim tools. The outputs show that employing the DPP system in local fog data centers reduced the power consumption for fog resource providers. [ABSTRACT FROM AUTHOR]

With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students' objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students' abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students' abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education. [ABSTRACT FROM AUTHOR]

Purpose: Although organizations have more data than ever at their disposal, actually deriving meaningful insights and actions from them is easier said than done. In this concern, the main objective of this study is to identify trends and research opportunities regarding data management within new product development (NPD) and collaborative engineering. Design/methodology/approach: Bibliometric and systemic analyses have been carried out using the methodological procedure ProKnow-C, which provides a structured framework for the literature review. A bibliographic portfolio (BP) was consolidated with 33 papers that represent the state of art in the subject. Findings: Most recent researches within the BP indicate new trends and paradigm shifts in this area of research, tackling subjects such as the internet of things, cloud computing, big data analytics and digital twin. Research gaps include the lack of data automation and the absence of a common architecture for systems integration. However, from a general perspective of the BP, the management of experimental data is suggested as a research opportunity for future works. Although many studies have tackled data and collaboration based on computer-aided technologies environments, no study examined the management of the measured data collected during the verification and validation stages of a product. Originality/value: This work provides a fresh and relevant source of authors, journals and studies for researchers and practitioners interested in the domain of data management applied to NPD and collaborative engineering. [ABSTRACT FROM AUTHOR]

The question this special issue would like to address is how to harvest big data to help decision-makers to deliver better fact-based decisions aimed at improving performance or to create better strategy? This special issue focuses on the big data applications in supporting operations decisions, including advanced research on decision models and tools for the digital economy. Responds to this special issue was great and we have included many high-quality papers. We are pleased to present 13 of the best papers. The techniques presented include data mining, simulation and expert system with applications span across online reviews, food retail chain to e-health. [ABSTRACT FROM AUTHOR]

This article seeks to conduct a bibliometric analysis focusing on exponential technologies such as big data, internet of thing (IoT), artificial intelligence (AI), blockchain and cloud computing. It aims to outline research trends in this domain and explore their correlation with resilience. The study aims to track the evolution of research trends in this field over time and identify less explored dimensions of exponential technologies. Leveraging performance analysis and science mapping techniques, the paper highlights the significant growth and potential in these areas, considering them as pivotal agendas of the twenty-first century. By examining scientific productivity metrics such as publications, authors, institutions, countries and keywords, the article offers insights into emerging areas within exponential technologies. As the first comprehensive study of its kind, it provides a broad overview of the main trends and patterns in resilience research encompassing big data, IoT, AI, blockchain and cloud computing, consolidating them into a single cohesive narrative. [ABSTRACT FROM AUTHOR]

With the vigorous development of big data and cloud computing, containers are becoming the main platform for running applications due to their flexible and lightweight features. Using a container cluster management system can more effectively manage multiocean containers on multiple machine nodes, and Kubernetes has become a leader in container cluster management systems, with its powerful container orchestration capabilities. However, the current default Kubernetes components and settings have appeared to have a performance bottleneck and are not adaptable to complex usage environments. In particular, the issues are data distribution latency, inefficient cluster backup and restore leading to poor disaster recovery, poor rolling update leading to downtime, inefficiency in load balancing and handling requests, poor autoscaling and scheduling strategy leading to quality of service (QoS) violations and insufficient resource usage, and many others. Aiming at the insufficient performance of the default Kubernetes platform, this paper focuses on reducing the data distribution latency, improving the cluster backup and restore strategies toward better disaster recovery, optimizing zero-downtime rolling updates, incorporating better strategies for load balancing and handling requests, optimizing autoscaling, introducing better scheduling strategy, and so on. At the same time, the relevant experimental analysis is carried out. The experiment results show that compared with the default settings, the optimized Kubernetes platform can handle more than 2000 concurrent requests, reduce the CPU overhead by more than 1.5%, reduce the memory by more than 0.6%, reduce the average request time by an average of 7.6%, and reduce the number of request failures by at least 32.4%, achieving the expected effect. [ABSTRACT FROM AUTHOR]

The growing complexity of security threats and the pervasive prevalence of cyberattacks have become more apparent in the present era, and the advent of big data, characterized by its distinctive features, has introduced layers of complexity to security tasks. Intrusion Detection Systems (IDSs) constitute a crucial line of defense, but their adaptation to the realm of big data is imperative. While traditional Machine Learning (ML)-based IDSs have been pivotal in detecting malicious patterns, they are often incapable to keep pace with the demands of expansive big data networks. This paper proposes a novel decentralized Multi-Agent Reinforcement Learning (MARL)-based IDS designed to address the specific challenges posed by big data. Our solution employs decentralized cooperative MARL, securing communicative channels throughout the detection process and concurrent data preprocessing which significantly reduces the overall processing time. Furthermore, the integration of Cloud computing and Big Data streaming techniques further facilitates real-time intrusion detection as cloud's resources allow rapid pre-process and analyse of massive data streams using powerful clusters. Likewise, Big Data streaming techniques ensure that potential intrusions are identified and addressed as they occur. Experimental results, conducted on the widely recognized NSLKDD benchmark dataset, demonstrate the superiority of our solution over other state-of-the-art approaches for big data networks, achieving an accuracy rate of 97.44%. [ABSTRACT FROM AUTHOR]

As one of the major constitutes of human society, education has been continuously producing a huge amount of data and become an important of sources of big data. Deeply mining and analyzing these big education data are of practical significance for optimizing education resource deployment and improving education quality. However, the big education data are often of diverse types and from multiple parties, which raises a big challenge for accurate and reasonable educational data fusion especially when the educational data are correlated with each other. In view of this challenge, we put forward a novel cloud computing-aided multi-type data fusion approach considering data correlation in education, to accommodate the big volume, diverse types and correlation of educational data. In concrete, the data fusion operation is mainly based on the Mahalanobis distances which can overcome the data diversity in multiple-dimensional data fusion for education. Afterwards, we provide a case study to show the concrete steps of our proposal. At last, a set of experiments are deployed to validate the feasibility of our proposal in this paper. Experimental results prove the effectiveness and efficiency of our approach in dealing with multi-type data fusion with correlation in education. [ABSTRACT FROM AUTHOR]