BigVis EDBT/ICDT 2018
Abstract. The amount and significance of time series that are associated with specific locations, such as visitor check-ins at various places or sensor readings, have increased in many domains over the last years. Although several works exist for time series visualization and visual analytics in general, there is a lack of efficient techniques for geolocated time series in particular. In this work, we present an approach that relies on a hybrid spatial-time series index to allow for interactive map-based visual exploration and summarization of geolocated time series data. In particular, we use the BTSR-tree index, which extends the R-tree by maintaining bounds for the time series indexed at each node. We describe the structure of this index and show how it can be directly exploited to produce map-based visualizations of geolocated time series at different zoom levels efficiently. We empirically validate our approach using two real-world datasets, as well as a synthetic one that is used to test the scalability of our method.