This article aims to present, adapt, and propose the use of the Balanced Scorecard (BSC) as a tool to support strategic management for sustainable cities. To achieve this goal, publications and 27 sustainable cities projects were also consulted, through project reports and programs from the cities themselves on the topic were consulted. A text mining analysis was applied to the results obtained in this literature review to find the main factors related to sustainable cities. To evaluate the findings of this first stage, consultations with specialists were carried out, specifically to assess the main factors. Based on the previous steps, and based on the original BSC, an adaptation of the tool was proposed, where its structure was changed to meet the needs of the strategic management of sustainable cities. It was concluded that creating a sustainable city requires understanding the difficulties faced by urban center managers with the implementation and management of institutional sustainable development practices. The BSC for sustainable cities can assist as a viable management strategic tool to more efficient use of resources in order to develop sustainable cities.
This article aims to analyze the impacts of the Fourth Industrial Revolution on the implementation of smart sustainable cities. For this purpose, a data mining process was conducted to analyze the terms that had a higher incidence in the literature in order to classify them by relevance and identify their interdependencies in the concepts of sustainable cities and smart cities. As a result, we highlight that the Fourth Industrial Revolution will have implications on several factors that are deeply connected to the success of cities in becoming sustainable: job creation, industries, innovation, environmental preservation, community involvement, and accessibility. In this context, policymakers will have opportunities and challenges that must be faced. Big data, the IoT, augmented reality, and simulations can have positive and negative externalities. Positive externalities include new information that could be mined, analyzed, and used for identifying previously unseen problems, the provision of new industrial innovations that can make economies thrive, helping promote inclusion for disabled people, as well as helping society to foresee problems and hence adapt to them in a timely manner.
BackgroundWeb-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.MethodWe focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.ResultsWe present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.ConclusionWe have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.
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