OBJECTIVE: The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts. MATERIALS AND METHODS: We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings. RESULTS: Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards. DISCUSSION: We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation. CONCLUSION: CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.

Trip recommendation has drawn considerable attention over the past decade. In trip recommendation, a sequence of point-of-interests (POIs) are recommended for a given query which includes an origin and a destination. Recently the emergence of the attention mechanism and many attention-incorporated models have achieved great success in various fields. Trip recommendation problems demonstrate similar characteristics that can potentially benefit from the attention mechanism. However, applying the attention mechanism for trip recommendation is non-trivial. We are motivated to answer the following two research questions. (1) How can we learn trip representation effectively without labels? Unlike most of the natural language processing tasks, there are no ground-truth labels available for trip recommendation. (2) How can we learn trip representation effectively without handcrafting negative samples? In this paper, we cast the trip representation learning into a natural language processing (NLP) task. We propose BERT-Trip, a self-supervised contrast learning framework, to learn effective and scalable trip representation in support of time-sensitive and user-personalized trip recommendation. BERT-Trip builds on a Siamese network to maximize the similarity between the augmentations of trips with BERT as the backbone encoder. We utilize the masking strategy for generating augmented views (positive sample pairs) of trips in the Siamese network and employ the stop-gradient on one side of the Siamese network to eliminate the need to use any negative sample pairs or momentum encoders. Extensive experiments on real-world datasets demonstrate that BERT-Trip consistently outperformed the state-of-the-art methods in terms of all effectiveness metrics. Compared with the state-of-the-art methods, BERT-Trip is able to yield up to 24 percent and 40 percent increases in F1score on the Flickr and the Weeplaces datasets, respectively. A rigorous performance evaluation of BERT-Trip on scalability up to 12800 POIs is also provided.

Skyline queries have drawn great interest and been widely used in various application domains including multi-criteria decision making, search pruning, and personalized recommendation systems. Given multiple criteria, skyline queries return objects that are not dominated by any other objects. As an extension of traditional skyline queries, probabilistic skyline queries aim to cope with uncertain datasets. This paper presents a novel MapReduce-based framework, ProbSky, in support of fast parallel evaluation of probabilistic skyline queries on large high-dimensional data. ProbSky efficiently evaluates exact p-skyline queries on large uncertain data without compromising the quality of query results. From the theoretical point of view, we formally prove two pruning lemmas integrated with ProbSky to strengthen the early pruning capacity. ProbSky builds on top of three optimization techniques, namely, dominant instance pruning, grid-based partitioning, and pivot point-based acceleration. Extensive experiments on both real and synthetic datasets unveil that compared to the state-of-the-art, ProbSky speeds up the evaluation of exact p-skyline queries on large high-dimensional data by at least one order of magnitude in most cases. Our experimental results also validate that by balancing the memory consumption and execution time among machines, ProbSky is adroit at curbing the bottleneck effect that causes severe system performance deterioration.

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce Overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. Despite a few machine-learning-based methods that have been proposed for detecting overprescribing, they usually ignore the patient prescribing behavior and their performances are not satisfying. In light of this, we propose a novel model RxNet for overprescribing detection in PDMP. RxNet builds a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various Rx entries (e.g., patients) whose representations are encoded by graph neural network. In addition, to explore the dynamic Rx-refill behavior and medical condition variation of patients, an RxLSTM network is designed to update representations of patients. Based on the output of RxLSTM, a dosing-adaptive network is leveraged to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a 1-year Ohio PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse, with an average of 5.7% and 7.3% improvement on F1 score respectively.

Music is present in numerous forms in our daily lives and is deemed essential to it. Multiple applications have been proposed to let users check into a location and tag that check-in with the song to which they are listening. This is time-consuming and requires much work in voluntary manual tagging. One of our major goals is to automatically determine the spatial scope of a song. Our research challenge is how to identify locations in unstructured and badly-cased lyric texts (e.g., all caps, camel case, non-cased, studly caps, etc.) that are mostly submitted by volunteers from all over the world. Uncertain casing leads to a severe performance drop when using named entity recognition (NER) and geographical information is often lost due to a failure to correctly identify geographical entities. We overcome this failure by normalizing the lyrics in the sense that the information loss is minimized and propose the MusicStand(http://musicstand.umiacs.io/) framework to process/input lyric text that involves three steps: cleaning, truecasing, and geotagging. MusicStand enables users to explore or search a music collection where the goal is to find and play songs about particular geographic entities (i.e., toponyms) using a map query interface. Note that the collection may be static (e.g., a songbook) or dynamic (e.g., a radio playlist).