Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
翻译:尽管我们在智能城市装置和系统方面达到了新的水平,但迄今为止的努力侧重于向智能城市服务消费者提供各种数据来源,同时忽视提供简化妥善利用这些数据的方法。在这方面,将给智能城市带来附加值的第一步是,通过异常点探测和数据批注在智能城市创造知识,同时以自动化和多方联动方式提供支持。我们在这里介绍“学习城市”这一解决方案,这一解决方案已被桑坦德现有智能城市部署和“有机城市实验-服务生态系统”验证。我们讨论了关键挑战以及典型使用案例,并报告了我们的设计和实施情况,以及将大型智能城市数据集与机器学习相结合的一些初步结果。