Big Data in Smart Cities

Khan, Zaheer; Peters-Anders, Jan

doi:10.1007/978-3-319-77525-8_33

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some works address the implementation of mechanisms to collect and access data from heterogeneous sources [16], while other works develop tools that implement efficient data processing [17,18] or develop algorithms for specific data analytic routines [19,20]. Geographic information is a key element in the exploitation of big data in smart cites because it provides contextual information for a myriad of applications [21]. Nevertheless, existing information systems and services offer software components with no or limited ability for spatial data analysis [16,22], even when spatial information plays a crucial role in decision making for many disciplines in the domain of smart cities [9,23].…”

Section: Introductionmentioning

confidence: 99%

Integration and Exploitation of Sensor Data in Smart Cities through Event-Driven Applications

Alvarez

Morales

Kraak

2019

Sensors

View full text Add to dashboard Cite

Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

show abstract