Abstract
Applications targeting Smart Cities tackle common challenges, however solutions are seldom portable from one city to another due to the heterogeneity of city ecosystems. A major obstacle involves the differences in the levels of available information. In this demonstration we present REMI, a reusable elements framework to handle varying degrees of information availability by design from two complementary angles, namely graceful degradation (GRADE) and data enrichment (DARE). In a nutshell, we develop reusable machine learning black boxes for mining and aggregating streaming data, either to infer missing data from available data, or to adapt expected accuracy based on data availability. We illustrate the proposed approach using tram data from the city of Warsaw.
Original language | English |
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Title of host publication | DEBS 2017 - Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems |
Publisher | Association for Computing Machinery, Inc |
Pages | 323-326 |
Number of pages | 4 |
ISBN (Electronic) | 9781450350655 |
DOIs | |
Publication status | Published - 8 Jun 2017 |
Event | 11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 - Barcelona, Spain Duration: 19 Jun 2017 → 23 Jun 2017 |
Conference
Conference | 11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 |
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Country/Territory | Spain |
City | Barcelona |
Period | 19/06/17 → 23/06/17 |
Keywords
- Data enrichment
- Graceful degradation
- Information availability