Cloud-based Big Data Analytics Research Articles

Big Data in Cloud Computing

The convergence of Big Data and cloud computing has revolutionized the way organizations process, store, and analyze large datasets. This paper explores the synergistic relationship between these two transformative technologies, highlighting their impact on business operations, decision-making, and innovation. By leveraging cloud platforms, enterprises can harness the scalability, flexibility, and cost-efficiency of distributed computing to manage vast volumes of data generated from diverse sources. Cloud-based big data analytics enables real-time insights, which drive strategic actions, improve customer experiences, and foster competitive advantages. The research delves into key cloud architectures, data management techniques, and analytics tools that underpin big data solutions, while addressing challenges such as data security, privacy, and compliance. Furthermore, the study reviews industry use cases across various sectors, illustrating how big data in cloud environments enhances productivity and innovation. The findings underscore the pivotal role of cloud computing in unlocking the full potential of big data, offering a roadmap for businesses aiming to capitalize on data-driven strategies.

Cloud computing for big data analytics How cloud computing can handle procesing large amounts of data and improve real-time data analytics

With the increasing volume, velocity, and variety of data generated by various sources, big data has become a critical challenge for many organizations. Cloud computing provides an efficient and cost-effective solution to address the challenges of big data analysis. In this paper, we review the state-of-the-art cloud computing technologies for big data analysis, including cloud storage, cloud computing platforms, and cloud-based big data analytics tools. We also identify the major challenges and solutions for cloud-based big data analysis.

To store or not: Online cost optimization for running big data jobs on the cloud

As businesses increasingly rely on cloud-based big data analytics services to drive insights, reducing the cost of storing and analyzing large volumes of data in the cloud has become a major concern. During the execution of big data analysis jobs, some of the generated data can be reused by subsequent jobs. By storing such intermediate data, the cost of running big data jobs can be greatly reduced for businesses using cloud services. An important challenge is how to determine which data should be stored in order to save costs. Existing storing strategies do not differentiate between data with different usage frequencies, resulting in significant storage costs in practical applications. To address the aforementioned challenges, in this paper we propose two online algorithms, one deterministic and the other randomized, which dynamically determine whether to store the data with the aim of saving cost. We show that our proposed deterministic algorithm (resp., randomized) incurs costs within a factor of 2−α′ (resp., 21+α′) times the minimum cost obtained by an optimal offline algorithm which is assumed to know the exact future a priori. Finally, through extensive experiments with real-world workload of big data jobs in Alibaba Cloud environment, we demonstrate that our proposed online algorithms can achieve significant cost savings under common cloud pricing schemes.

Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development

Harnessing the Power of Cloud-Based Big Data Analytics for E-Government Advancement in Morocco: A Catalyst for Development

Reproducible and Portable Big Data Analytics in the Cloud

Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first is how to automate end-to-end scalable execution of analytics including distributed environment provisioning, analytics pipeline description, parallel execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics.

Cloud-Based Bigdata Analytics for Reshaping Operational Efficiency in Smart Cities

Cloud-Based Bigdata Analytics for Reshaping Operational Efficiency in Smart Cities

Case Studies and Best Practices in Cloud-Based Big Data Analytics for Process Control

In this research paper, case studies and exemplars and lessons learnt in cloud-based big data analytics for process control are reviewed. The paper presents big data, cloud computing and industrial process control system with prospects of enhancing effectiveness, increasing production rates, and effective decision making in the industries. The research in this paper involves a comprehensive literature review of the research topic, and an extension of the analysis to four specific business industries as well as a discussion of architectural elements for cloud-based big data solutions for process control business. It also presents various crucial issues such as data protection, adherence to legal requirements, and compatibility with other systems, giving solutions. In addition, the research compares the effectiveness of cloud-based solutions with on-premise ones and discuss other novelties, including edge computing and artificial intelligence as the tendencies potentially influencing process control. Consequently, the findings of this research can be helpful for both industry practitioners and researchers who aim to optimize process control and organization operation with the help of cloud-based big data analytics

A Practical Guide to an Open-Source Map-Matching Approach for Big GPS Data

This work shows how map-matching helps to minimize errors in GPS data by finding the most probable corresponding points of the recorded waypoints of a trajectory on a road network. We investigate an open-source alternative for map-matching trajectories called Valhalla, which could replace limited and costly commercial map-matching services. Valhalla is an open-source routing engine, which provides different services, such as path-finding, map-matching, and generating maneuvers based on a path. We build a cloud-based big data analytics framework on Amazon Web Services (AWS) platform for map-matching. This well-established framework is scalable and could process millions of trajectories. Using an example GPS dataset, it is demonstrated how Valhalla can be used for map-matching at scale. The dataset consists of about 18 million trips in the year 2019 that have at least one recorded point in a bounding box surrounding Frankfurt am Main. The map-matching results confirm an adequate performance of Valhalla map-matching, show a reduction of errors by distance calculation, and allow for further street-segment-based analysis.

Cloud-based big data analytics integration with ERP platforms

PurposeThe importance of integrating cloud-based big data analytics software with enterprise resource planning (ERP) platforms is not clearly understood. Specifically, this study aims to look into firms that implemented SAP during the boom of ERP implementations. Further, this study aims to look into the type of cloud-based big data analytics software that those firms installed when cloud-based packages started to be available.Design/methodology/approachThis study specifically looks at productivity and the sources of productivity, such as technical progress and efficiency change, using a non-parametric approach that does not constrain the analysis to any production function.FindingsThis study found that by the time cloud-based big data analytics software started to be available, SAP-adopters already had a competitive advantage over the non-SAP adopters manifested through productivity and specifically through technology and not efficiency. Later, when the same firms decided to integrate their ERP platforms with cloud-based big data analytics software, the firms that had installed SAP already had an initial advantage over the non-SAP-adopters.Research limitations/implicationsIn support of the theory of technology organization environment (Tornatzky and Fleisher, 1990) and Posner's theoretical framework (Posner, 1961), a cloud-based big data analytics software will not change the relative position that firms have in the industry, so a cloud-based big data analytics software by itself will not provide a competitive advantage over competitors. Still, it will ensure that the preliminary technological gap that SAP-adopters already had is not magnified.Practical implicationsKnowing the sources of productivity improvement and technological improvements will give managers greater leverage when negotiating budgets, negotiating long-term contracts in better terms and in the decision process.Originality/valueThis study fills a research gap by looking into the implementation of a cloud-based big data analytics software with ERP.

Toward Reliability-Satisfied and Hop-Constrained Path Backuping for Cloud-Based Big Data Analytics in Industrial Internet

Toward Reliability-Satisfied and Hop-Constrained Path Backuping for Cloud-Based Big Data Analytics in Industrial Internet

Toward Reliability-Satisfied and Hop-Constrained Path Backuping for Cloud-Based Big Data Analytics in Industrial Internet

Toward Reliability-Satisfied and Hop-Constrained Path Backuping for Cloud-Based Big Data Analytics in Industrial Internet

Novel dynamic load balancing algorithm for cloud-based big data analytics

Novel dynamic load balancing algorithm for cloud-based big data analytics

Cloud-based big data analytics platform using algorithm templates for the manufacturing industry

ABSTRACTSpecialised knowledge on analytics algorithms is necessary to conduct a data analysis; thus, there are limits to how a manufacturing manager who is not a data analysis expert conducts analysis tasks. This study proposed a manufacturing-specialised data analytics algorithm template and a cloud-based big data analytics platform to resolve such a problem and to enable those not specialising in data analysis to perform analysis tasks. The manufacturing-specialised data analytics algorithm template is a concept in which data characteristics are considered according to manufacturing problems to systematically provide the pre-processing, data mining, and visualisation algorithms required for conducting analysis tasks. A cloud-based big data analytics platform is designed to use algorithm templates for analysis and is capable of not only storing and managing data per company but also creating analysis models and confirming results. The platform consists of the following: inter-connected middleware, data storage, a batch manager, a data analytics modeller, and an analysis engine. This study applied the proposed manufacturing-specialised data analytics algorithm template and cloud-based big data analytics platform to the machine problem-solving process executed by mobile phone component production companies. It also sought study limitations and offered development plans for future research.

IBiopsy® for Precision Medicine

A high-throughput artificial intelligence-powered image-based phenotyping platform, iBiopsy® (Median Technologies, Valbonne, France), which aims to improve precision medicine, is discussed in the presented review. The article introduces novel concepts, including high-throughput, fully automated imaging biomarker extraction; unsupervised predictive learning; large-scale content- based image-based similarity search; the use of large-scale clinical data registries; and cloud-based big data analytics to the problems of disease subtyping and treatment planning. Unlike electronic health record-based approaches, which lack the detailed radiological, pathological, genomic, and molecular data necessary for accurate prediction, iBiopsy generates unique signatures as fingerprints of disease and tumour subtypes from target images. These signatures are then merged with any additional omics data and matched against a large-scale reference registry of deeply phenotyped patients. Initial applications targeted include hepatocellular carcinoma and other chronic liver diseases, such as nonalcoholic steatohepatitis. This new disruptive technology is expected to lead to the identification of appropriate therapies targeting specific molecular pathways involved in the detected phenotypes to bring personalised treatment to patients, taking into account individual biological variability, which is the principal aim of precision medicine.

Opportunity and Challenges for Migrating Big Data Analytics in Cloud

Big Data Analytics is a big word now days. As per demanding and more scalable process data generation capabilities, data acquisition and storage become a crucial issue. Cloud storage is a majorly usable platform; the technology will become crucial to executives handling data powered by analytics. Now a day’s trend towards “big data-as-a-service” is talked everywhere. On one hand, cloud-based big data analytics exactly tackle in progress issues of scale, speed, and cost. But researchers working to solve security and other real-time problem of big data migration on cloud based platform. This article specially focused on finding possible ways to migrate big data to cloud. Technology which support coherent data migration and possibility of doing big data analytics on cloud platform is demanding in natute for new era of growth. This article also gives information about available technology and techniques for migration of big data in cloud.

Leveraging Cloud Based Big Data Analytics in Knowledge Management for Enhanced Decision Making in Organizations

In recent past, big data opportunities have gained much momentum to enhance knowledge management in organizations. However, big data due to its various properties like high volume, variety, and velocity can no longer be effectively stored and analyzed with traditional data management techniques to generate values for knowledge development. Hence, new technologies and architectures are required to store and analyze this big data through advanced data analytics and in turn generate vital real-time knowledge for effective decision making by organizations. More specifically, it is necessary to have a single infrastructure which provides common functionality of knowledge management, and flexible enough to handle different types of big data and big data analysis tasks. Cloud computing infrastructures capable of storing and processing large volume of data can be used for efficient big data processing because it minimizes the initial cost for the large-scale computing infrastructure demanded by big data analytics. This paper aims to explore the impact of big data analytics on knowledge management and proposes a cloud-based conceptual framework that can analyze big data in real time to facilitate enhanced decision making intended for competitive advantage. Thus, this framework will pave the way for organizations to explore the relationship between big data analytics and knowledge management which are mostly deemed as two distinct entities.

Context Aware Middleware Architectures: Survey and Challenges.

Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work.

The Rise of Data Capital

If exploited correctly, data can offer economic advantage, spur innovations, and generate revenue to enterprises. Cloud computing and big data are often intertwined. In fact, big data services is becoming a general phrase, with examples including cloud-based big data analytics and cloud-based big data storage services. This issue looks at various topics in cloud computing, from the emerging cloud of sensors, which builds on the Internet of Things and wireless sensors, to cloud brokering and its challenges when deploying in an enterprise, to a unique method to classify and grade security aspects.

Cloud Based Big Data Analytics Framework for Face Recognition in Social Networks Using Machine Learning

Face recognition (FR) has been at the crux of several novel breakthroughs over the past two decades and has steadily proffered several cross-domain applications that range from mainstream commercial software to critical law enforcement applications. Recent groundbreaking developments in Big Data analysis , Cloud Computing , Social Networks and Machine learning have vastly transformed the conventional view of how several formidable problems in Computer Vision can be tackled. Hence in this paper, we will provide a thorough survey of the concepts of Cloud Computing, Big Data, Social networks and Machine Learning from a contemporary perspective of FR,and proffer a framework for a novel FR approachbased on the Extreme Learning Machines technique to perform the task of Face Tagging for Social Networks operating on Big Data. In the proposed approach, the desirable properties of the aforementioned concepts are amalgamated to form an effective coalition that can augment the performance of FR, in addition to serving immeasurably in a plethora of other disciplines.