Research Notes: Smart City Control Room Dashboards: Big Data Infrastructure, from data to decision support

2018 ◽  
Vol 4 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Daniele Cenni ◽  
Mino Marazzini ◽  
Nicola Mitolo ◽  
Paolo Nesi ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Smart City ◽  
Control Room ◽  
Data Infrastructure

scholarly journals Smart City Control Room Dashboards Exploiting Big Data Infrastructure (S)

2018 ◽  
Author(s):  
Paolo Nesi ◽  
Pierfrancesco Bellini ◽  
Mino Marazzini ◽  
Nicola Mitolo ◽  
Michela Paolucci ◽  
...  
Keyword(s):  
Big Data ◽  
Smart City ◽  
Control Room ◽  
Data Infrastructure

scholarly journals Digitization and urban governance: The city as a reflection of its data infrastructure

2021 ◽  
pp. 002085232110332
Author(s):  
Ali Bayat ◽  
Peter Kawalek
Keyword(s):  
Big Data ◽  
Smart City ◽  
Urban Governance ◽  
Smart Cities ◽  
Engaged Scholarship ◽  
Data Governance ◽  
Data Infrastructure ◽  
Big Data Technologies ◽  
Data Infrastructures ◽  
The City

This article introduces the ‘House Model’, an integrated framework consisting of four data governance modes, based on the urban and smart city vision, context, and big data technologies. The model stems from engaged scholarship, synthesizing and extending the academic debates and evidence from existing smart city initiatives. It provides a means for comparing cities in terms of their digitization efforts, helps the planning of more effective urban data infrastructures and guides future empirical research in this area. The article contributes to the literature examining the issue of big data and its governance in local government and smart cities. Points for practitioners Data is a vital part of smart city initiatives. Where the data comes from, who owns it and how it is used are all important questions. Data governance is therefore important and has consequences for the overall governance of the city. The House Model presented in this article provides a means for organizing data governance. It relates questions of data governance to the history and vision of smart city initiatives, and provides a typology organizing these initiatives.

scholarly journals Cultivating Urban Big Data

2021 ◽  
pp. 547-565
Author(s):  
Ningchuan Xiao ◽  
Harvey J. Miller
Keyword(s):  
Big Data ◽  
Data Structure ◽  
Smart City ◽  
General Public ◽  
Urban Infrastructure ◽  
Data Infrastructure ◽  
Collaborative Environment ◽  
Wide Range ◽  
Agile Approach ◽  
Smart Mobility

AbstractUrban big data often contain spatial and temporal elements that have increasingly become an integral part of various applications and projects such as smart mobility, smart city, and other digitally enhanced urban infrastructure. It is critical to develop an open and collaborative environment so that these data can be used by a wide range of users. This chapter first discusses some characteristics and sources of urban big data. Three hypothetical user stories are described to highlight the potential of these data. After describing the internal data structure of these data and techniques that can be used to retrieve the data, we discuss the difficulty in making the data useful for the general public and elaborate on a self-organizing agile approach to developing an urban big data infrastructure.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool

Implementasi Metode Entropy dan Topsis Dalam Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik

2018 ◽  
Vol 10 (1) ◽  
pp. 13-18
Author(s):  
Mitha Anggreani Rupang ◽  
Adhi Kusnadi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Smart City ◽  
Entropy Method ◽  
Satisfaction Level ◽  
Topsis Method ◽  
Alphabetical Order ◽  
Index Terms ◽  
Growth Ability

Employee is a part of the company's most important asset in its efforts to maintain survival, growth, ability to compete and profit. At this time the process of assessment of employees in Jakarta Smart City is still in the form of manual and the decision only from one party only, so the process is still not accurate. So it takes the methods that must be able to replace that system. For that reason, a Decision Support System (SPK) was created to determine the best employees in Jakarta Smart City. In the system implemented the method of Entropy and TOPSIS. Entropy method can be trusted in determining the weight of the criteria to be used. And TOPSIS method can quickly perform the ranking process. Criteria to be used are quality and quantity of work, obedience, cooperation, morale, and work discipline. The index of satisfaction level of respondents to the decision support system ranges from 70% -80%, meaning that the assessment of the system created gives results at a fairly good level. Index Terms—employee, nter key words or phrases in alphabetical order, separated by commas

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