@inproceedings{8a12005962774c688552bfa669eb1929,
title = "UFO-Tracker: Visualizing UFO sightings",
abstract = "analyzing geospatial and temporal observations are common tasks for many application domains. In this paper, we introduce UFO-Tracker, a visual analytic tool for analyzing unidentified flying object sightings from the National UFO Reporting Center. The goal here is to give the user a higher level view of where different types of sightings occur, to investigate whether sightings are increasing or decreasing over time, to discover the connections between different events which might happen at different geographic areas, and to quickly identify typical incidents at a given period of time without reading the whole sightings through topic modelling. Multiple visualization and data mining techniques are combined to make sense the increasingly large UFO reports which get updated hourly. The usefulness of the application is evaluated through a case study where anon-expert in ufology can find some typical interesting sightings. Our application can also be able to detect some misleading events such as missile launch or fireworks on a specific day through keywords and topic extraction. One limitation of our application is the data which is not up-to-date when new sightings are posted since the application pulled and processed data locally. Our initial application targets UFO sighting reports. However, we believe our approach has wider applications in other research domains, such as analyzing text corpus obtained from social media.",
keywords = "Unidentified Flying Objects, dot plots, geospatial temporal visualizations, topic modelings, word clouds",
author = "Nguyen, {Vinh T.} and Vung Pham and Tommy Dang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Big Data, Big Data 2018 ; Conference date: 10-12-2018 Through 13-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/BigData.2018.8622418",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4352--4359",
editor = "Naoki Abe and Huan Liu and Calton Pu and Xiaohua Hu and Nesreen Ahmed and Mu Qiao and Yang Song and Donald Kossmann and Bing Liu and Kisung Lee and Jiliang Tang and Jingrui He and Jeffrey Saltz",
booktitle = "Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018",
}