| Title | Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams |
|---|---|
| ID_Doc | 39212 |
| Authors | Kolozali, S; Bermudez-Edo, M; FarajiDavar, N; Barnaghi, P; Gao, F; Ali, MI; Mileo, A; Fischer, M; Iggena, T; Kuemper, D; Tonjes, R |
| Title | Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams |
| Year | 2019 |
| Published | Ieee Internet Of Things Journal, 6, 2 |
| Abstract | An increasing number of cities are confronted with challenges resulting from the rapid urbanization and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage, and provide plans for public resources and infrastructures in city environments, while offering citizens and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several issues, such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic integration of data streams, and complex event processing on top of real-time data aggregation and quality analysis in a semantic Web environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open Data Aarhus by the City of Aarhus. We examine the framework utilizing symbolic aggregate approximation to reduce the size of data streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimization of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques. |
| http://repository.essex.ac.uk/23170/1/bare_jrnlFinal.pdf |
Similar Articles
| ID | Score | Article |
|---|---|---|
40118
|
Ordonez-Ante, L; Van Seghbroeck, G; Wauters, T; Volckaert, B; De Turck, F Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities(2020)Sensors, 20, 9 | |
| 45328
|
Nesi, P; Po, L; Viqueira, JRR; Trillo-Lado, R An Integrated Smart City Platform(2018) | |
| 36227
|
Girtelschmid, S; Steinbauer, M; Kumar, V; Fensel, A; Kotsis, G On the application of Big Data in future large-scale intelligent Smart City installations(2014)International Journal Of Pervasive Computing And Communications, 10, 2 | |
| 45066
|
Bellini, P; Nesi, P; Paolucci, M; Zaza, I Smart City architecture for data ingestion and analytics: processes and solutions(2018) | |
| 38538
|
Silva, BN; Khan, M; Han, K Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making(2017) | |
| 45065
|
Komamizu, T; Amagasa, T; Shaikh, SA; Shiokawa, H; Kitagawa, H Towards Real-time Analysis of Smart City Data: A Case Study on City Facility Utilizations(2016) | |
| 38338
|
Soomro, K; Khan, Z; Hasham, K Towards Provisioning of Real-time Smart City Services Using Clouds(2016) | |
| 37146
|
Gledson, A; Dhafari, TB; Paton, N; Keane, J A smart city dashboard for combining and analysing multi-source data streams(2018) | |
| 36547
|
Farmanbar, M; Rong, CM Triangulum City Dashboard: An Interactive Data Analytic Platform for Visualizing Smart City Performance(2020)Processes, 8, 2 | |
| 40206
|
De Maio, C; Fenza, G; Loia, V; Orciuoli, F Distributed online Temporal Fuzzy Concept Analysis for stream processing in smart cities(2017) |