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Automated business analytics for artificial intelligence in Big Data@X 4.0 era
Published in Matthias Dehmer, Frank Emmert-Streib, Frontiers in Data Science, 2017
Reference 4 points out, “the novelty in such a scenario is not in a new technology, but in that it combines the available technology in a new way. The availability of bulk data allows various new business models. In combination with third-party services such as weather, calendar, payment services, geolocation, or historical data, new levels of organization and scheduling are possible.” Like all members of the as a service family, Data as a Service (DaaS) builds on the concept that the product (data in this case) can be provided on demand to the user regardless of geographic or organizational separation of provider and consumer. In addition, the emergence of service-oriented architecture has also rendered the actual platform on which the data reside irrelevant. This development has enabled the emergence of the relatively new concept of DaaS.
Security, integrity, and privacy of cloud computing and big data
Published in Muhammad Imran Tariq, Valentina Emilia Balas, Shahzadi Tayyaba, Security and Privacy Trends in Cloud Computing and Big Data, 2022
Muhammad Salman Mushtaq, Muhammad Yousaf Mushtaq, Muhammad Waseem Iqbal, Syed Aamer Hussain
DaaS goes hand in hand with the IaaS by providing virtualized storage to the consumers. Considered as a particular case of IaaS, DaaS allows organizations to get rid of the on-site database systems linked with keeping dedicated servers, licenses, services, and maintenance [63]. In DaaS, the consumers only must pay for usage rather than for the full license in the case of the on-site storage systems. For handling large datasets, some DaaS provides abstractions intended to level, store, and recover a large quantity of data inside a short time. Frameworks under DaaS are Amazon S3, Google Bigtable, and Apache HBase [64].
Advances in Urban Remote Sensing
Published in Guoqing Zhou, Urban High-Resolution Remote Sensing, 2020
The cloud computing system effectively places all types of data resources, computing resources, storage resources, and other resources in the “cloud” node, to process big and various data, and to provide “cloud” users with sharing and payment services in areas such as data, computing, storage, and others. The four types of cloud services provided are:IaaS (Infrastructure as a Service). This type of service mainly provides users with some basic virtual technology services, including computing, storage, network communication, and server storage. Users can use these services through the network with payment for a variety of applications. Typical services vendors include Go Grid, Amazon EC2, Drop Box, and Akamai (Li 2013; Wang et al. 2013; Youssef 2012).PaaS (Platform as a Service). This type of service provides users with a platform with a programming environment, development tools, and other combinations operated in the cloud node (Geetha and Robin 2017). The users can operate, develop, publish, distribute, and share software resources through the platform service. Typical platform service vendors include Google application engine, Alibaba cloud, Baidu cloud, and Microsoft Azure (Buyya et al. 2014; Espadas et al. 2013).SaaS (Software as a Service). In this type of service, users can directly access and use the software resources placed in the cloud node through the network in accordance with their own needs. The users do not need to install the software or obtain software licenses, and just pay pocket fees. This form of service greatly saves resources. The typical service vendors include YouTube, Zoho, Google Apps, and Salesforce.com (Li et al. 2015; Rao et al. 2012; Yang et al. 2015).DaaS (Data as a Service). This type of service can provide users with a comprehensive and rich visualization through a browser with large, multi-dimensional data. Typical service vendors are Google Earth, Google Map, and Yahoo Map (Sénica et al. 2011; Zhu et al. 2016).
The framework design of smart factory in discrete manufacturing industry based on cyber-physical system
Published in International Journal of Computer Integrated Manufacturing, 2020
Gaige Chen, Pei Wang, Bo Feng, Yihui Li, Dekun Liu
According to the typical data protocol of discrete product smart factory, the adaptive range of the protocol is determined. The application frequency, range and number of devices of each data transmission protocol are comprehensively analysed. The number and content of typical and common data protocols are determined as the automatic adaptation range of intelligent terminals to data protocols. Edge algorithm and lightweight embedded transplantation method are studied to form an edge algorithm suitable for intelligent terminal, which is implemented by programming language and embedded in intelligent terminal. By embedding in the intelligent terminal, it realises the collection, control, optimisation and other functions of the edge end. And the user interface, perception agent, decision-making agent, control agent, cloud access agent and other functions needed for the quality problem, fault problem diagnosis and predictive maintenance of automatic response of smart factory application scenarios are completed. On the basis of providing real-time operation data for the service cloud, it realises the local predictive automatic response of remote monitoring and control of smart factory production process and reduces the cloud computing burden.
Interoperability in cloud manufacturing: a case study on private cloud structure for SMEs
Published in International Journal of Computer Integrated Manufacturing, 2018
Xi Vincent Wang, Lihui Wang, Reinhold Gördes
To recap, multiple CM approaches are developed in the past years, and the manufacturing devices are integrated in different ways. However, there is limited research work on the CM system’s interoperability, especially when considering the complex composition of hardware and software. In the CM system, data and computing service interoperability is less challenging as the cloud developers can continue the success of the interoperable computing systems. However, the manufacturing process interoperability is more critical as the hardware is involved at multiple levels. The CM system needs to handle the interactions among hardware and software applications. Additionally, when SMEs attempt to adopt CM system, they often face extra difficulties like confidentiality agreement, limited IT resource and so forth. Hence in the next section, the CM system is firstly developed towards full interoperability across the four levels mentioned above. Then the proposed system is specifically tailored to meet the requirements of SMEs.
A scalable cloud-based cyberinfrastructure platform for bridge monitoring
Published in Structure and Infrastructure Engineering, 2019
Seongwoon Jeong, Rui Hou, Jerome P. Lynch, Hoon Sohn, Kincho H. Law
A properly designed data management framework that can support effective use of bridge monitoring data is an indispensable component of data-intensive SHM systems (Law, Smarsly, & Wang, 2014). This paper describes the design and implementation of a cloud-based cyberinfrastructure platform that brings together information modelling, data management, web service and cloud computing technologies to facilitate the workflow and deployment of SHM systems.