Join Operation for Semantic Data Enrichment of Asynchronous Time Series Data

Abstract

In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns.

Global Models of Smart Cities and Potential IoT Applications: A Review

“…These cities are all working to develop and implement policies that will support their smart city visions. However, there is still some work to be done in terms of data sharing and cybersecurity [211].…”

“…As shown in Table 8, examine how the smart city collects, stores, and analyzes massive data generated by smart technologies. Assess the level of data security and privacy protection measures in place, as well as transparency in data usage and consent mechanisms [211,237,238].…”

“…Open data: Amsterdam has made a large amount of data publicly available, which enables researchers and developers to create innovative solutions to urban problems. The city has also established an open data portal that provides access to a wide range of datasets [211,213].…”

“…Data-driven decision making: Copenhagen utilizes data and digital technologies to make informed decisions and improve city services. The city collects and analyzes data on various aspects, including energy consumption, transportation patterns, and air quality, to identify areas for improvement and implement targeted solutions [211,222,223]. 6.…”

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

“…Illustrative case studies offer concrete insights into the practical application of these technologies. In Barcelona's smart city initiative, semantic technologies facilitated data integration from diverse domains such as public transportation, energy consumption, and citizen services, thereby enhancing urban planning and operational efficiency [23]. Similarly, Singapore's semantic traffic management system illustrates how machine learning, intertwined with ontology-driven analytics, can enhance real-time traffic flow management, reducing congestion and improving urban mobility [24].…”