标题： 海报摘要：稀疏协作感知应用的动态数据驱动预测模型 显示英文标题

标题： Poster Abstract: A Dynamic Data-Driven Prediction Model for Sparse Collaborative Sensing Applications

摘要： 协作感知中的一个基本问题在于提供对关键事件（例如，危险的环境条件、城市异常、经济趋势）的准确预测。 然而，由于资源限制，协作感知应用通常仅从部分物理位置收集测量数据，并预测其余位置的测量数据。 这个问题被称为稀疏协作感知预测。 在本海报中，我们通过利用主题建模和在线学习技术提出了一种新的闭环预测模型。 我们使用一个真实世界的协作感知数据集来评估我们的方案。 初步结果表明，我们提出的方案优于最先进的基线方法。 显示英文摘要

摘要： A fundamental problem in collaborative sensing lies in providing an accurate prediction of critical events (e.g., hazardous environmental condition, urban abnormalities, economic trends). However, due to the resource constraints, collaborative sensing applications normally only collect measurements from a subset of physical locations and predict the measurements for the rest of locations. This problem is referred to as sparse collaborative sensing prediction. In this poster, we present a novel closed-loop prediction model by leveraging topic modeling and online learning techniques. We evaluate our scheme using a real-world collaborative sensing dataset. The initial results show that our proposed scheme outperforms the state-of-the-art baselines.