A Knowledge-Enhanced Modular Method for Predicting Electric Vehicle Remaining Driving Range under Cold Conditions Utilizing Cloud-Based Big Data

Big Data is used to find new value and brings us several benefits, which we didn't know before. Various analytics has been studied in Big Data area for benefits. Moreover, to reduce analysis time and to support real-time service to user, distributed processing can be an alternative solution. Big Data also requires high performance resource for distributed analysis, for this reason, Big Data and Cloud Computing seem to be naturally combined-called Cloud-based Big Data. However, Cloud-based Big Data had no criteria to evaluation. Also, it is hard to decide which Cloud-based Big data is well designed and how much resource should be provided to provide qualitied Cloud-based Big data service. Hence, in this paper, we surveyed enterprises and their product and then, deduct criteria to classify and evaluate Cloud-based Big Data service.

Optimal construction of virtual networks for Cloud-based MapReduce workflows

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.

Big Data's 5 V complexities are making it increasingly difficult to develop an understanding of the end to end process. Big Data platforms play a crucial role in many critical systems, combining with Internet-of-Things, Artificial Intelligence and Business Analytics. It is both relevant and important to understand Big Data systems to identify the best tools that fit the requirements of heterogeneous platforms. The objective of this paper is to "discover" a set of design principles and rules for Cloud-based Big Data platforms for complex, heterogeneous environments. The design scope comprises Big Data's significance, challenges and architectural impacts. Using a methodology Reverse Engineered Design Science Research (REDSR), artifacts from leading vendors are used to elicit the design principles and rules with relevant details of Big Data components. We conclude that the findings are relevant and useful for DevOps architects and practitioners in operating complex, heterogeneous Cloud-based Big Data platforms.

Battery health diagnostics: Bridging the gap between academia and industry

Continuous increase in urban population causes enormous pressure on the limited resources of cities including transport, energy, water, housing, public services, and others. Hence, the need to plan and develop smart cities-based solutions for enhanced urban governance is becoming more evident. The technological framework for smart cities services connect hundreds of data collecting device networks (e.g., sensor networks) with central server or cloud through Internet. Scheduling of these enormous data (or big data) both at the device networks and central server/cloud is significantly important to facilitate timely and priority-based smart city services. This paper introduces a cloud- based big data framework and Priority-based, Dynamic and Time sensitive data processing and Scheduling (PDTS) approach that works both for the device networks and cloud-based big data framework. Simulation results demonstrate that the proposed PDTS approach reduces the number of data transmission and data processing time as opposed to data scheduling only in cloud-based big data framework.

The architecture of the data center is the main key for producing a highly functional demand big data management platform for multipurpose uses. Nowadays various technologies have come to provide construction of that purpose, providing several use cases for big data analytics and processing. In this paper, we want to explore possibilities of architecture that had to be built in answer to the multipurpose data center, such as analytical research, scientific simulation, machine learning, deep data learning, and data orchestration. We discover how Hadoop and its element supporter can be used alongside cloud orchestrators such as Terraform or Occopus and container orchestrators such as Kubernetes or Docker Swarm. We also provide possible supporting components that can handle the different jobs in High-Performance Computing and how the system can be secured. Our proposed approach in this research has developed the architecture for cloud-based big data management for multipurpose computation.

Big Data is a collection of large amount of data which is growing very rapidly with the popularity of social networking sites. The size of the Big data has been extended from terabytes to petabytes. Big data are characterized by four important attributes: volume, velocity, variety and veracity. The volume attributes describe the data at rest in the range from terabytes to Exabyte's, the velocity deals with data in motion, i.e streaming the data to respond within milliseconds rather than in seconds, the variety discuss the data in many different forms such as structured, unstructured, text and multimedia data, whereas the veracity deals with the data in doubt, i.e. uncertainty in data due to data inconsistency. These attributes of big data make it a challenge for organizations to have control over, and use such data. In today's era we are overloaded with the information, however we are lacking the insight. The 90% of the data in the world today has been created in the last two years by various social networking sites. As big data grows it challenges the capabilities of traditional data warehouses that collect and store large amounts of internal and external data. Data drawn from these repositories are used to improve decision making, increase organizational efficiencies, and improve organizational effectiveness. In this paper an attempt has been made to explore the real life application of Big Data , cloud database, Hadoop, Map Reduce and Cloud Computing, in various domains.

<strong class="journal-contentHeaderColor">Abstract.</strong> Although the generally high fertility of volcanic soils is often seen as an opportunity, short-term consequences of eruptions on natural and cultivated vegetation are likely to be negative. The empirical knowledge obtained from post-event impact assessments provides crucial insights into the range of parameters controlling impact and recovery of vegetation, but their limited coverage in time and space offers a limited sample of all possible eruptive and environmental conditions. Consequently, vegetation vulnerability remains largely unconstrained, thus impeding quantitative risk analyses. Here, we explore how cloud-based big Earth observation data, remote sensing and interpretable machine learning (ML) can provide a large-scale alternative to identify the nature of, and infer relationships between, drivers controlling vegetation impact and recovery. We present a methodology developed using Google Earth Engine to systematically revisit the impact of past eruptions and constrain critical hazard and vulnerability parameters. Its application to the impact associated with the tephra fallout from the 2011 eruption of Cordón Caulle volcano (Chile) reveals its ability to capture different impact states as a function of hazard and environmental parameters and highlights feedbacks and thresholds controlling impact and recovery of both natural and cultivated vegetation. We therefore conclude that big Earth observation (EO) data and machine learning complement existing impact datasets and open the way to a new type of dynamic and large-scale vulnerability models.

The purpose of this paper is to do a comprehensive analysis of Cloud based Big Data challenges and opportunity for SMEs in India. Big Data (BD) are big buzz words, and many large enterprises are making significant advances in their product development, customer relations, and increasing profitability by targeting specific customers. Therefore, it has generated interest for both the academic and industry world as it has the potential to enhance the decision making process by providing valuable insights. The large organisations are benefiting from using BD as a tool to gain a competitive edge over rivals by driving new revenue streams and improving internal operational efficiency. The large organisations are already investing as well leveraging BD. On the contrary, Small and Medium Enterprises (SMEs) in India are slow adopters of new technologies due to many factors and pose to lose their competitive edge on the world stage. SMEs can also leverage BD to gain a strategic competitive advantage but are resource poor in terms of investment in manpower and financial resources. Major challenges still exist in the adoption of BD especially in information technology architecture. Cloud Computing (CC) can eliminate the need to maintain expensive computing hardware, software, and networking resources. CC can also provide a solution to scalability issues, high speed performance, and fault tolerance for using BD. With CC usage, SMEs can take advantage of BD without significant investment in information technology and manpower. This research uses secondary data as a research data collection technique, which is its limitation. However, a quantitative research methodology using statistical surveys is planned to be conducted in the near future to understand the factors impacting on the adoption of Cloud based Big Data for Indian SMEs. This paper contributes to exploring and enhancing the understanding of the challenges and opportunities for SMEs in India for adopting BD. This study will also have both industrial implications for practitioners in India, as well as those in other developing countries, and academic implications for researchers who are interested in BD adoption in developing countries.&#x0D; Keywords: Cloud Computing (CC), Big Data (BD), Small and Medium Enterprises (SMEs), Challenges, Opportunities, India.

In this paper, we expose an energy optimization method for an ensemble of electrical vehicles. The idea is based on vehicles information sharing in a cloud database. Installed sensors in a vehicle will allow us to know the needed vehicle information. The regrouped data will be used then for building an optimal energy experience which will be shared with the new vehicles. The neural network technique is used here for the learning phases. The proposed application for this work was validated for five connected cars. Matlab Simulink was used for simulating the results and given the present statistics.

Industrial big data is an important part of big data family, which has important application value for industrial production scheduling, risk perception, state identification, safety monitoring and quality control, etc. Due to the particularity of the industrial field, some concepts in the existing big data research field are unable to reflect accurately the characteristics of industrial big data, such as what is industrial big data, how to measure industrial big data, how to apply industrial big data, and so on. In order to overcome the limitation that the existing definition of big data is not suitable for industrial big data, this paper intuitively proposes the concept of big data cloud and the 3M (Multi-source, Multi-dimension, Multi-span in time) definition of cloud-based big data. Based on big data cloud and 3M definition, three typical paradigms of industrial big data applications are built, including the fusion calculation paradigm, the model correction paradigm and the information compensation paradigm. These results are helpful for grasping systematically the methods and approaches of industrial big data applications.

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.

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.

With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.