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We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of-the- art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics. 

We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of the-art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics. 

We released open-source software Hadoop-GIS in 2011, and presented and published the work in VLDB 2013. This work initiated the development of a new spatial data analytical ecosystem characterized by its large-scale capacity in both computing and data storage, high scalability, compatibility with low-cost commodity processors in clusters and open-source software. After more than a decade of research and development, this ecosystem has matured and is now serving many applications across various fields. In this paper, we provide the background on why we started this project and give an overview of the original Hadoop-GIS software architecture, along with its unique technical contributions and legacy. We present the evolution of the ecosystem and its current state-of-the-art, which has been influenced by the Hadoop-GIS project. We also describe the ongoing efforts to further enhance this ecosystem with hardware accelerations to meet the increasing demands for low latency and high throughput in various spatial data analysis tasks. Finally, we will summarize the insights gained and lessons learned over more than a decade in pursuing high-performance spatial data analytics. 

Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases. Meanwhile, large amounts of narrative clinical notes provide complementary information, but are often not integrated into predictive models. In this paper, we provide a novel multimodal transformer to fuse clinical notes and structured EHR data for better prediction of in-hospital mortality. To improve interpretability, we propose an integrated gradients (IG) method to select important words in clinical notes and discover the critical structured EHR features with Shapley values. These important words and clinical features are visualized to assist with interpretation of the prediction outcomes. We also investigate the significance of domain adaptive pretraining and task adaptive fine-tuning on the Clinical BERT, which is used to learn the representations of clinical notes. Experiments demonstrated that our model outperforms other methods (AUCPR: 0.538, AUCROC: 0.877, F1:0.490). 

Text correction on mobile devices usually requires precise and repetitive manual control. In this paper, we present EyeSayCorrect, an eye gaze and voice based hands-free text correction method for mobile devices. To correct text with EyeSayCorrect, the user first utilizes the gaze location on the screen to select a word, then speaks the new phrase. EyeSayCorrect would then infer the user’s correction intention based on the inputs and the text context. We used a Bayesian approach for determining the selected word given an eye-gaze trajectory. Given each sampling point in an eye-gaze trajectory, the posterior probability of selecting a word is calculated and accumulated. The target word would be selected when its accumulated interest is larger than a threshold. The misspelt words have higher priors. Our user studies showed that using priors for misspelt words reduced the task completion time up to 23.79% and the text selection time up to 40.35%, and EyeSayCorrect is a feasible hands-free text correction method on mobile devices.