A data-driven Smart City Transformation Model utilizing the Green Knowledge Management Cube

Knowledge management (KM) is a systematic way of managing the organization's assets for creating valuable knowledge that can be used across the organization to achieve the organization's success. A broad category of technologies that allows for gathering, storing, accessing, and analyzing data to help business users make better decisions, business intelligence (BI) allows analyzing business performance through data-driven insight. Business analytics applies different methods to gain insight about the business operations and make better fact-based decisions. Big data is data with a huge size. In the chapter, the authors have tried to emphasize the significance of knowledge management, business intelligence, business analytics, and big data to justify the role of them in the existence and development of an organization and handling big data for a virtual organization.

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A Conceptual Framework for Effective Knowledge Management Using Information and Communication Technologies

International Journal of Knowledge and Systems Science ◽

10.4018/jkss.2010040105 ◽

2010 ◽

Vol 1 (2) ◽

pp. 49-61 ◽

Cited By ~ 4

Author(s):

Hepu Deng

Keyword(s):

Knowledge Management ◽

Conceptual Framework ◽

Information And Communication Technologies ◽

Communication Technologies ◽

Transformation Model ◽

Information And Communication ◽

Knowledge Objects ◽

Multi Method Approach ◽

Role Of Information

This paper investigates the role of information and communication technologies in enabling and facilitating the conversion of knowledge objects in knowledge management and explores how these roles might be affected in an organization. Such an investigation is based on a critical analysis of the relationships between data, information and knowledge, leading to the development of a transformation model between data, information and knowledge. Using a multi-method approach, in this paper, the author presents a conceptual framework for effective knowledge management in an organization. The author discusses the implications of the proposed framework for designing and developing knowledge management systems in an organization.

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Data-Driven Approach for Incident Management in a Smart City

Applied Sciences ◽

10.3390/app10228281 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8281

Author(s):

Luís B. Elvas ◽

Carolina F. Marreiros ◽

João M. Dinis ◽

Maria C. Pereira ◽

Ana L. Martins ◽

...

Keyword(s):

Data Mining ◽

Smart City ◽

City Planning ◽

Smart Cities ◽

Data Driven ◽

Incident Management ◽

Cascading Effects ◽

Data Mining Approach ◽

Industry Standard ◽

Data Driven Approach

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better.

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Human-Centric Data Science for Urban Studies

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8120584 ◽

2019 ◽

Vol 8 (12) ◽

pp. 584 ◽

Cited By ~ 2

Author(s):

Bernd Resch ◽

Michael Szell

Keyword(s):

Urban Studies ◽

Smart City ◽

Data Science ◽

City Planning ◽

Data Driven ◽

Data Sets ◽

Use Of Technology ◽

New Perspective ◽

Research Initiatives ◽

Adequate Data

Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens’ needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use.

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