scholarly journals Conceptualization and Non-Relational Implementation of Ontological and Epistemic Vagueness of Information in Digital Humanities

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 20 ◽  
Author(s):  
Patricia Martin-Rodilla ◽  
Cesar Gonzalez-Perez
Keyword(s):  
Big Data ◽  
Conceptual Framework ◽  
Digital Humanities ◽  
Natural Sciences ◽  
Data Systems ◽  
Modelling Language ◽  
Processing Power ◽  
Classical Studies ◽  
Implementation Guidelines ◽  
Big Data Systems

Research in the digital humanities often involves vague information, either because our objects of study lack clearly defined boundaries, or because our knowledge about them is incomplete or hypothetical, which is especially true in disciplines about our past (such as history, archaeology, and classical studies). Most techniques used to represent data vagueness emerged from natural sciences, and lack the expressiveness that would be ideal for humanistic contexts. Building on previous work, we present here a conceptual framework based on the ConML modelling language for the expression of information vagueness in digital humanities. In addition, we propose an implementation on non-relational data stores, which are becoming popular within the digital humanities. Having clear implementation guidelines allow us to employ search engines or big data systems (commonly implemented using non-relational approaches) to handle the vague aspects of information. The proposed implementation guidelines have been validated in practice, and show how we can query a vagueness-aware system without a large penalty in analytical and processing power.

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

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.

scholarly journals Enterprise service bus architecture for the big data systems

2014 ◽  
Vol 5 (1) ◽  
pp. 28-31 ◽  
Author(s):  
Cezary Orłowski ◽  
Edward Szczerbicki ◽  
Jan Grabowski
Keyword(s):  
Big Data ◽  
Data Systems ◽  
Flow Mechanism ◽  
Enterprise Service Bus ◽  
City Hall ◽  
Decision Making System ◽  
Processing Resources ◽  
Big Data Systems ◽  
Bus Architecture ◽  
The City

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.

Sign in / Sign up

Export Citation Format

Share Document