scholarly journals Computational Approaches for Urban Design Within the MENA Region

2021 ◽  
Author(s):  
Jaouad Akodad ◽  
Mohammed Bakkali ◽  
Mounir Ghogho
Keyword(s):  
Big Data ◽  
Urban Design ◽  
Design Process ◽  
Efficient Solutions ◽  
Data Driven ◽  
Urban Systems ◽  
Mena Region ◽  
Computational Tools ◽  
Computational Approaches ◽  
Data Driven Approach

This paper focuses on the use of computational tools to develop a data driven approach for an analytical study about different urban systems. This “framework” examines urban Big Data in the old medina of Sale in Morocco. The computational tools are more effective to provide insights within complexity, becoming a key to generate more efficient solutions throughout the design process. The findings of this study highlight the potential of a data driven approach to explore analytical aspects and move further to generative design using algorithms.

scholarly journals Out of the loop? On the radical and the routine in urban big data

Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Computational Models ◽  
Data Driven ◽  
Urban Systems ◽  
Out Of The Loop ◽  
Urban Futures ◽  
Urban Behaviour ◽  
The City

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.

scholarly journals Extracting Value from Industrial Alarms and Events: A Data-Driven Approach Based on Exploratory Data Analysis

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2772 ◽  
Author(s):  
Aguinaldo Bezerra ◽  
Ivanovitch Silva ◽  
Luiz Affonso Guedes ◽  
Diego Silva ◽  
Gustavo Leitão ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Exchange ◽  
Data Science ◽  
Exploratory Data Analysis ◽  
Data Driven ◽  
Industrial Data ◽  
Industrial Big Data ◽  
Data Driven Approach ◽  
Exploratory Data

Alarm and event logs are an immense but latent source of knowledge commonly undervalued in industry. Though, the current massive data-exchange, high efficiency and strong competitiveness landscape, boosted by Industry 4.0 and IIoT (Industrial Internet of Things) paradigms, does not accommodate such a data misuse and demands more incisive approaches when analyzing industrial data. Advances in Data Science and Big Data (or more precisely, Industrial Big Data) have been enabling novel approaches in data analysis which can be great allies in extracting hitherto hidden information from plant operation data. Coping with that, this work proposes the use of Exploratory Data Analysis (EDA) as a promising data-driven approach to pave industrial alarm and event analysis. This approach proved to be fully able to increase industrial perception by extracting insights and valuable information from real-world industrial data without making prior assumptions.

scholarly journals Real-Time Supply Chain Simulation: A Big Data-Driven Approach

2019 ◽  
Author(s):  
Antonio A.C. Vieira ◽  
Luis M.S. Dias ◽  
Maribel Y. Santos ◽  
Guilherme A.B. Pereira ◽  
Jose A. Oliveira
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Real Time ◽  
Data Driven ◽  
Data Driven Approach ◽  
Supply Chain Simulation

scholarly journals Big Data-Driven Approach for Health Inequalities in Foreign Patients with Injuries Visiting Emergency Rooms

2020 ◽  
Vol 26 (1) ◽  
pp. 34
Author(s):  
Jin Young Kang ◽  
Jinhee Kwon ◽  
Chang Hwan Sohn ◽  
Youn-Jung Kim ◽  
Hyo Won Lim ◽  
...  
Keyword(s):  
Big Data ◽  
Health Inequalities ◽  
Data Driven ◽  
Emergency Rooms ◽  
Data Driven Approach

scholarly journals Adoption of Big Data by Global Chemical Industries

2021 ◽  
Author(s):  
Ashiff Khan ◽  
A Seetharaman ◽  
Abhijit Dasgupta
Keyword(s):  
Big Data ◽  
Data Science ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Time Data ◽  
New Era ◽  
Potential Applications ◽  
Data Driven Approach ◽  
Process Plants ◽  
The Right

The new era of Big Data (BD) is influencing the chemical industries tremendously, providing several opportunities to reshape the way they operate and for shifting towards smart manufacturing. Given the availability of free software, and the large amount of real-time data generated and stored in process plants why many chemical industries are still not fully adopting BD? The industry is just starting to realize the importance of a large amount of data that they own to make the right decisions and to support their strategies. This article is exploring the importance of professional competencies and data science that influence BD in chemical industries for shifting towards smart manufacturing in a fast and reliable manner. This article utilizes a literature review and identifies potential applications in the chemical industry to shift from conventional methods towards a data-driven approach.

Apollo: Rapidly Picking the Optimal Cloud Configurations for Big Data Analytics Using a Data-Driven Approach

2021 ◽  
Vol 36 (5) ◽  
pp. 1184-1199
Author(s):  
Yue-Wen Wu ◽  
Yuan-Jia Xu ◽  
Heng Wu ◽  
Lin-Gang Su ◽  
Wen-Bo Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

2020 ◽  
Vol 9 (12) ◽  
pp. 752
Author(s):  
Anna Kovacs-Györi ◽  
Alina Ristea ◽  
Clemens Havas ◽  
Michael Mehaffy ◽  
Hartwig H. Hochmair ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Science ◽  
Temporal Analysis ◽  
Data Sources ◽  
Urban Systems ◽  
Urban Dynamics ◽  
Analysis Methods ◽  
Data Driven Approach ◽  
Spatio Temporal

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

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