The evolution of data-driven algorithms for trajectory analysis operations relies heavily on the availability of trajectory data. Unfortunately, most of the available trajectory datasets are not suitable for use by analysis operations. A main reason is that such trajectories are released in their raw form: A sequence of locations coming from the same device over a time period (e.g.: hours or years), whereas trajectory analysis operations need trip trajectories. Hence, existing trajectory analysis techniques preprocess the raw trajectories by applying simple rules to extract trips out of each trajectory. However, such basic rules miss too many realistic scenarios and result in low accuracy which negatively affects down-stream trajectory applications. This paper presents TrajSplit: an accurate and scalable algorithm for trip extraction from raw GPS trajectories. TrajSplit goes beyond the basic simple rules to introduce a realistic definition of a trip, which can be realized through a computationally expensive brute force approach. Therefore, TrajSplit offers two scalable heuristic approaches, that still achieve a very similar accuracy to its brute force. Experimental results, based on two real datasets, show that TrajSplit: (a) is far more accurate than the basic rules, and (b) is highly scalable when employing either of the heuristics.

Though polar scientists entertain having huge amounts of publicly available datasets, they face the challenge that working with such data is a cumbersome process that requires downloading tons of unnecessary data and writing various scripts on top of it. This hinders their ability to perform any kind of interactive analysis. This paper presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Experimental results with a real Polaris prototype and real scientific data show that it achieves highly interactive and scalable data access, enabling interactive analysis of polar science data.

This demonstration presents Polaris; a novel open-source system infrastructure for Polar science that is highly Interactive and Scalable. Polaris is designed based on three observations that distinguish the query workload of polar scientists, namely, all queries are spatio-temporal, not all data are equal, and the large majority of queries are aggregates. With this, Polaris is equipped with a hierarchical spatio-temporal index structure that stores precomputed aggregates for data of interest. Audience will be able to experience Polaris through various scenarios that show the interactivity and scalability as well as Polaris optimized query processes.

With the surge of data-driven solutions for trajectory analysis operations, the need for accurate trajectory trip data has spiked. However, the available datasets are raw trajectories spanning from hours to years, not representing actual trips for downstream applications. Therefore, pre-processing steps, such as basic rules to extract trips, are needed to use the datasets. However, this paper demonstrates that the current pre-processing steps are not enough and result in low accuracy, negatively affecting the downstream applications. This paper presents an overview of an accurate and scalable algorithm for splitting raw trajectories for trip extraction. We go beyond the basic rules to introduce a realistic definition of a trip and offer two scalable heuristics over the exhaustive brute force approach of the algorithm with similar accuracy. Experimental results show that the proposed algorithm is: (a) far more accurate than the basic rules, (b) scalable when employing either of the heuristics.