Resumen
Mobile telecoms operators possess an enormous quantity of data, which could be used to reduce the cost of installing new infrastructure, to provide a better QoS or to plan their infrastructure. Thus, they are concerned to model, understand and predict SMS and calls activity levels in their infrastructures. Besides, SMS and call activities analysis can open new business opportunities for geomarketing as well as trade area analysis. In the present effort, we detected activity zones with a difference of only 0.5 km from the reference activity areas extracted from Geo-Tweets. We also used Markov chains to represent and predict SMS and call activity levels, achieving a prediction success rate between 80% and 90%.
| Idioma original | Inglés |
|---|---|
| Páginas | 130-139 |
| Número de páginas | 10 |
| Estado | Publicada - 1 ene. 2016 |
| Evento | CEUR Workshop Proceedings - Duración: 1 ene. 2017 → … |
Conferencia
| Conferencia | CEUR Workshop Proceedings |
|---|---|
| Período | 1/01/17 → … |
ODS de las Naciones Unidas
Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible
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ODS 9: Industria, innovación e infraestructura
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ODS 11: Ciudades y comunidades sostenibles
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ODS 17: Alianzas para lograr los objetivos
Huella
Profundice en los temas de investigación de 'DAZIO: Detecting activity zones based on input/output call and SMS activity'. En conjunto forman una huella única.Citar esto
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