Software Engineering for Big Data Systems

IEEE Software ◽  
2016 ◽  
Vol 33 (2) ◽  
pp. 32-35 ◽  
Author(s):  
Ian Gorton ◽  
Ayse Basar Bener ◽  
Audris Mockus
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.


2014 ◽  
Vol 5 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Cezary Orłowski ◽  
Edward Szczerbicki ◽  
Jan Grabowski

Abstract This paper presents the construction of the enterprise service bus architecture in data processing resources for a big data decision-making system for the City Hall in Gdansk. The first part presents the key processes of bus developing: the installation of developing environment, the database connection, the flow mechanism and data presentation. Developing processes were supported by models: KPI (Key Processes Identifier) and SOP (Simple Operating Procedures) (also connected to the bus). The summary indicates the problems of the bus construction, especially processes of routing, conversion, and handling events.


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