Cloud computing and big data: Technologies and applications

The advanced information technologies resulting from the rapid growth of location-based services (LBSs) have greatly enriched people's urban lives. In particular, locationbased social networks (LBSNs) allow users to check in and share their travel tips with various smart devices anytime and anywhere, which could result in big data problems. On the one hand, it makes tourism data not only larger but also more difficult to store and manage. On the other hand, tourists have to spend a long time collecting information on points of interest (POIs) from the Internet and making customized trip schedules before traveling. To provide a more pleasant trip service, the paper proposes a cyber-physical system for touristcentric customized trip recommendation, named Trip-CPS based on cloud computing and big data technology. This system consists of a data-collection module for user-generated data (UGD) and contextual data, a data fusion and management module for multisource heterogeneous data storage, fusion and computing. And a recommendation module provides tourists with appropriate trips in suitable times and places. The techniques of cloud computing and big data can be used to enhance the performance of smart tourism, so tourists can enjoy various personalized trip recommendations

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

Cloud Computing and Big Data continue to be disruptive forces in computing and have made inroads in the Computer Science curriculum, with courses in Cloud Computing and Big Data being routinely offered at the graduate and undergraduate level. One major challenge in offering courses in Big Data and Cloud Computing is resources. The question is how to provide students with authentic experiences making use of current Cloud and Big Data resources and tools and do so in a cost effective manner. Historically, three options, namely physical clusters, virtual clusters and cloud-based clusters, have been used to support Big Data and Cloud Computing courses. Virtual clusters and cloud-based options are those that institutions have typically adopted and many arguments in favor of these options exist in the literature, citing cost and performance. Here we argue that teaching Big Data and Cloud Computing courses can be done making use of a physical cluster and that many of the existing arguments fail to take into account many important factors in their calculations. These factors include the flexibility and control of a physical cluster in responding to changes in industry, the ability to work with much larger datasets, and the synergy and broad applicability of an appropriately equipped physical cluster for courses such as Cloud Computing, Big Data and Data Mining. We present three possible configurations of a physical cluster which span the spectrum in terms of cost and provide cost comparisons of these configurations against virtual and cloud-based options, taking into account the unique requirements of an academic setting. While limitations do exist with a physical cluster and it is not an option for all situations, our analysis and experience indicates that there is great value in using a physical cluster to support teaching Cloud Computing and Big Data courses and it should not be dismissed.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.

SummaryScience Gateways provide scientists with tools for creating, executing, and monitoring scientific experiments on multiple resource infrastructures. Apache Airavata abstracts interactions between gateways and distributed computing infrastructures like Extreme Science and Engineering Discovery Environment, international grids, and campus clusters. Airavata consists of several component services such as the API server, Orchestrator, Workflow Interpreter, Credential Store, and Application Factory. In addition, Airavata uses third party software, including RabbbitMQ for messaging, MySQL for production database management, and Apache Zookeeper for internal communications. In this paper, we discuss our initial experiences with leveraging open source technologies to manage Airavata and its dependent components to deploy, detect, and restart failed components in an auto‐scaling platform. Such capabilities will allow Airavata services to be deployed in a wide area, large Virtual Machine (VM) based cluster, and a developer's laptop. The emerging technologies in cloud computing and Big Data that address these needs are the following: Docker, Marathon, and Apache Mesos. Docker is a Linux‐based lightweight container that allows different applications to run isolated from each other but safely share the machine's resources. Docker images of applications can be published in registries and retrieved for execution in the target infrastructures. Marathon provides a cluster‐wide init and control system for services, including Docker containers. Mesos provides a cluster‐wide framework to schedule tasks based on fine‐grained resource needs. Mesosphere provides the packages, scripts, and web interface to ease the use of these technologies. We present the design, experience, and lessons learned from integrating Mesos, Docker, and Marathon with Apache Airavata. Copyright © 2015 John Wiley & Sons, Ltd.

Academia, industry and government as well, are involved in big data projects. Many researches on big data applications and technologies are actively being conducted. This paper presents a literature review of recent researches on key technologies and open issues for big data management via cloud computing. Its goal is to identify and evaluate the main technology components and their impacts on cloud-based big data implementations. This is achieved by reviewing 40 publications published in the latest four years, 2014–2017. We classified the results based on the main technical aspects: frameworks, databases and data processing techniques, and programming languages. This paper also provides a reference source for researchers and developers, to determine the best emerging technologies for big data project implementation.

To select best papers published in 2013 in the field of big data and smart health strategies, and summarize outstanding research efforts. A systematic search was performed using two major bibliographic databases for relevant journal papers. The references obtained were reviewed in a two-stage process, starting with a blinded review performed by the two section editors, and followed by a peer review process operated by external reviewers recognized as experts in the field. The complete review process selected four best papers, illustrating various aspects of the special theme, among them: (a) using large volumes of unstructured data and, specifically, clinical notes from Electronic Health Records (EHRs) for pharmacovigilance; (b) knowledge discovery via querying large volumes of complex (both structured and unstructured) biological data using big data technologies and relevant tools; (c) methodologies for applying cloud computing and big data technologies in the field of genomics, and (d) system architectures enabling high-performance access to and processing of large datasets extracted from EHRs. The potential of big data in biomedicine has been pinpointed in various viewpoint papers and editorials. The review of current scientific literature illustrated a variety of interesting methods and applications in the field, but still the promises exceed the current outcomes. As we are getting closer towards a solid foundation with respect to common understanding of relevant concepts and technical aspects, and the use of standardized technologies and tools, we can anticipate to reach the potential that big data offer for personalized medicine and smart health strategies in the near future.

Traditional business can't provide suitable decision for manager, and can't provide personal service for customer. With computing and network technology development, cloud computing and big data technology now is popularly used in many fields. This paper provides a smart business cloud system based on Hadoop. Firstly, it uses Hadoop to build up a cloud computing system, it can provide powerful storage and computing ability, then uses big data mining to analyze these huge data and get rules or knowledge. For a smart business system, it not only need compute or analyze, it need provide ways to collect data and push message to customers. In this smart system, it provides the module that can collect data by many sensors and network, and uses Android system to push personal and valuable message to different customers.

ENERGY EFFICIENT ROBUST BACTERIAL FORAGING ROUTING PROTOCOL (EE-RBFRP) FOR BIG DATA NETWORK

In this paper, wavelet analysis methods, mainly including wavelet basis function, continuous and discrete wavelet transform and multiresolution analysis were introduced first. Then, current applications of the wavelet analysis methods to various aspects of cloud computing and big data were summarized. The three aspects of wavelet-based analysis include: The application of wavelet denoising, the online monitoring for image and the image watermarking algorithm design in cloud computing. For big data, we discuss from two aspects: The function approximation ability and the image processing in big data, several suggestions and opinions on the future researches and applications of wavelet analysis methods in cloud computing and big data were discussed.

In recent years, cloud computing has developed rapidly under the vigorous promotion of industry and academia. With the expansion of cloud computing, users' special needs for cloud resources have gradually improved. As a business model, cloud computing must pay more attention to user demands for services and provide users with high-quality services. As one of the key technologies in cloud computing, task scheduling is mainly responsible for assigning user tasks to the appropriate resources. However, the existing scheduling algorithms do not take full account of users' different needs. In this paper, we consider multidimensional QoS requirements, and introduce Berger model to judge the fairness of the resource allocation results. We also improve the Particle Swarm Optimization(PSO) algorithm by adjusting its parameters dynamically and making the position coding discrete. Then, we propose a task scheduling algorithm based on QoS-DPSO. The simulation results show that this algorithm can effectively carry out user tasks and reflect more fairness.

The digital revolution has profoundly reshaped the landscape of global finance, giving rise to an era where speed, security, and intelligence define the success of financial services. Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services aims to illuminate this transformation by exploring disruptive technologies at the core of today’s financial evolution. In recent years, Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and Big Data have moved from theoretical concepts to practical tools that power every facet of modern finance—from algorithmic trading and fraud detection to credit scoring and personalized banking. These innovations are not just enhancing operational efficiency; they are enabling real-time decision-making, improving financial inclusion, and reshaping the way individuals, businesses, and institutions engage with money. This book is crafted for a wide spectrum of readers including financial professionals, technologists, researchers, policy-makers, and students. It unpacks the mechanisms behind intelligent financial systems, provides real-world case studies, and discusses emerging trends such as decentralized finance (DeFi), embedded banking, digital identity, and AI-driven risk assessment. Special attention is given to the regulatory, ethical, and cybersecurity challenges that come with this rapid digitalization. What makes this work timely is its holistic view of innovation—not just as a product of technology, but as a driver of systemic change in global economies. From digital wallets and instant cross-border payments to cloud-native banking platforms, the book outlines how intelligent technologies are setting new standards for agility, transparency, and customer experience. As financial ecosystems grow increasingly complex and interconnected, this book serves as a compass guiding stakeholders toward building secure, inclusive, and intelligent financial futures.

The recent adoption of cloud computing, Web 2.0 (web as a platform), and Big Data technologies have become the main driver of the paradigm shift. For higher education, choosing the right platform for a next generation of Learning Management System (LMS) namely LMS 2.0 is becoming more important than choosing a tool in the new paradigm. This chapter discusses factors for higher institution in determining a future direction for its LMS to take advantage of pervasive knowledge management, efficiency and effectiveness of operations. Literature studies have deployed for this study to portray the state of future LMS initiative. We found that the trends of cloud computing and big data will be predominant factor in viewing future LMS adoption and implementation. LMS 2.0 can be a solution to make learning systems in a higher education is flexible in terms of resources adoption, quality of learning, knowledge management, and implementation.

The aim of the paper is to present the new factors of ERP (Enterprise Resource Planning system) implementation related to the usage of cloud-based and blockchain information technology and to discover potential possibilities of using cloud computing, big data, and blockchain technologies in the methodology of ERP implementation. The article covers the characteristics of the current implementation methodologies and also discusses how these technologies can be adopted in the implementation process. During the study, a literature review was carried out and survey-based interviews with project managers were performed. Previous studies examined in the literature review have some limitations. Most of them are focused on ERP performance in the cloud or measure success factors of ERP implementation. A research gap exists regarding the impact of technologies in the implementation process and the results of this process. The research shed some light on cloud computing and blockchain in ERP implementation. The conclusions are as follows: (1) Some methods of sustainable methods of ERP implementation with the use of cloud technologies, blockchain, and big data have been examined in research studies, but there is still a lack of publications with in-depth analyses of the impact of blockchain on ERP implementation success. (2) The practitioners recommended the new technologies based on cloud computing and big data in the process of ERP implementation, and more knowledge about blockchain would have a positive impact on the success of sustainable ERP implementation as well as on the willingness to adopt the new technologies.