Informace o publikaci
A Scalable Spatio-Temporal Analytics Framework for Urban Networks
| Autoři | |
|---|---|
| Rok publikování | 2023 |
| Druh | Článek ve sborníku |
| Konference | Networks in the Global World VI :Proceedings of NetGloW 2022 |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://link.springer.com/chapter/10.1007/978-3-031-29408-2_5 |
| Doi | http://dx.doi.org/10.1007/978-3-031-29408-2_5 |
| Klíčová slova | Analytics framework; Spatio-temporal transactional data; Spatio-temporal transactional networks; Urban networks |
| Popis | Numerous real-world processes and events, especially in the urban domain, are represented with spatio-temporal transactional data (STTD): examples include human and vehicle mobility, communication, and economic transactions. Despite the overwhelming variability of such data sets they can be seen as having very much in common, including their structure as well as the analytic and visualization challenges they face. The present paper describes an idea of an analytic platform implementing an underlying general data model as well as the analytic and modeling tools for STTD. The platform is intended to provide increased scalability of the STTD-driven applications enabling broad reuse of the most common analytic and visualization solutions in multiple contexts of urban analytics. |