III: Small: Adopting Machine Learning Techniques for Big Spatial and Spatio-temporal Data and Applications

从包括手机、GPS功能的车辆和卫星在内的各种设备收集的无处不在的空间和位置数据及其规模的推动下，该奖项旨在利用这些数据来增强各种人工智能和机器学习技术的可扩展性、准确性和可用性。这将推动这些技术的最新发展，不仅适应空间数据占主导地位的更多应用(例如交通、气象和农业)，而且适应当前处理空间数据的应用(例如人口普查、知识库建设)。该奖项采用机器学习技术来处理大的空间和时空数据，并在两个相互垂直但相关的方向上应用。首先，在机器学习技术和应用程序中注入空间意识，这将导致此类应用程序的准确性更高。第二，利用机器学习技术的最新进展，提高长期空间和时空数据分析技术的可用性、可扩展性和准确性。为了达到这个目标，该奖项探索了三个主要的研究主题：(1)发展概念，通过在马尔可夫逻辑网络中注入空间意识来构建空间马尔可夫逻辑网络(SMLN)。(2)利用开发的SMLN为空间感知的知识库建设系统构建第一个开源系统。(3)利用开发的SMLN来提高几个长期空间分析问题的准确性、可扩展性和可用性，包括空间自回归、空间分类和贝叶斯网络。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Motivated by the ubiquity and magnitude of spatial and location data collected from myriad of devices, including, cell phones, GPS-enabled vehicles, and satellites, this award aims to take advantage of such data to enhance the scalability, accuracy, and usability of various artificial intelligence and machine learning techniques. This will advance the state-of-the-art of these techniques not only to accommodate more applications where spatial data is predominant (e.g., transportation, meteorology, and agriculture), but also to current applications that deal with spatial data (e.g., census, knowledge-base construction).This award adopts machine learning techniques for big spatial and spatio-temporal data and applications in two orthogonal, but related, directions. First, injecting the spatial awareness inside machine learning techniques and applications, which would result in a higher accuracy for such applications. Second, taking advantage of the recent advances in machine learning techniques to boost the usability, scalability, and accuracy of long lasting spatial and spatio-temporal data analysis techniques. Towards its goal, this award exploits three main research topics: (1) Developing the concept and building Spatial Markov Logic Network (SMLN) by injecting the spatial awareness inside Markov Logic Network. (2) Leveraging the developed SMLN to build the first open-source system for a spatially-aware knowledge base construction system. (3) Leveraging the developed SMLN to increase the accuracy, scalability, and usability of several long lasting spatial analysis problems, including spatial autologistic regression, spatial classification, and Bayesian networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.