| Title |
Time Series Data Management Optimized For Smart City Policy Decision |
| ID_Doc |
57410 |
| Authors |
Colosi M.; Martella F.; Parrino G.; Celesti A.; Fazio M.; Villari M. |
| Year |
2022 |
| Published |
Proceedings - 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2022 |
| DOI |
http://dx.doi.org/10.1109/CCGrid54584.2022.00068 |
| Abstract |
The European project URBANITE (Supporting the decision-making in URBAN transformation with the use of disruptive Technologies) aims to put in place a sustainable mobility with the support of disruptive and innovative technologies for the sector of urban mobility. Urban mobility and smart mobility contexts, but not only, now require more than ever the use of large amounts of historical data to carry out the necessary analyses for different use cases. A good management of time series data, able to use pagination concepts in an optimized way and providing the user with specifications functions, therefore become indispensable. This need emerged as a native implementation in MongoDB 5.0. With the release of this version, users have functionality to manage time series collections. This new solution has stimulated us to undertake a study on the methods of managing time series data and compare the solution proposed by MongoDB with our solution based on the advanced use of the bucket approach. The two solutions were tested in a real context and the results obtained are reported in the paper. © 2022 IEEE. |
| Author Keywords |
Big Data; database No-SQL; Decision Support; MongoDB; Policy Decision; Smart City; Smart Mobility; Time series; Urban Mobility |
