In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
The process of modeling a city is complex. The intrinsic complexity of interactions between city entities make it very difficult to map relevant sets of dynamic aspects that are often used to characterize a city. Moreover, these entity interactions, along with the numerous entities and processes, differ from one city to another. Thus, the process of modeling the city is typically use-case centered, where the modeling is performed towards a specified goal. This approach, hence, streamlines the process, identifying which characteristics need to be modeled. The work in @cite_1 proposes a core conceptual model for the Domain Knowledge Model of a Smart City, which originally involves multiple domains and cities. The proposed work aims to support cross-domain and cross-city interoperability by specifying terms from different stakeholders. Ontologies play a big role in enabling cross-city comparison. The Semantic Web has been used in the Open Government Data (OGD) approach to make it possible for cities to share information and knowledge under a common vocabulary. Pushing this further, the GCI (Global City Indicators) Ontology @cite_7 is an effort for the modeling of city entities that covers the concepts used by global indicators using Semantic Web technologies.
@cite_1 @cite_7
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As the emergence of the requirement of building Smart Cities across many countries, the Domain Knowledge Model of Smart City (Short as DKM4SC) becomes necessary. The Domain Knowledge Model of Smart City has abundant contents and involves many domains and cities. In order to support cross-domain and cross-city interoperation of knowledge, we should concise the common concepts and their relationship from domains and cities and construct a core concept model (short as CCM of DKM4SC) that can specify terms from different stakeholders, support semantic understanding and give standard knowledge expression. The model is proposed to represent the core concept of Smart City for exchanging data, service. A case study is presented to illustrate the usage of the model. Abstract This paper addresses the problem of how city indicators and their supporting data are to be published on the Semantic Web so that automated analysis can be performed. With the publishing of ISO 37120, cities have a standard set of indicators that can be used to compare their performance. The problem is that no standards exist for publishing indicators on the Semantic Web. In this paper we introduce the Global City Indicator Ontology (GCIO). The GCIO addresses five issues: 1) how is meta-data associated with a single indicator value represented? 2) how are indicator definitions represented? 3) how is the data used to derive an indicator value represented? 4) how is indicator theme specific knowledge represented, and 5) how is city specific knowledge represented? The GCIO has been implemented and validated using the City of Toronto ISO 37120 indicators reported for 2013. Research continues in developing ontologies specifically for each of the indicator themes such as: Education, Shelter, Health, Transportation and Innovation. The Smart City concept is being developed from a lot of different axes encompassing multiple areas of social and technical sciences. However, something that is common to all these approaches is the central role that the capacity of sharing information has. Hence, Information and Communication Technologies ICT are seen as key enablers for the transformation of urban regions into Smart Cities. Two of these technologies, namely Internet of Things and Big Data, have a predominant position among them. The capacity to "sense the city" and access all this information and provide added-value services based on knowledge derived from it are critical to achieving the Smart City vision. This paper reports on the specification and implementation of a software platform enabling the management and exposure of the large amount of information that is continuously generated by the IoT deployment in the city of Santander. Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.