A Scalable Framework for Accelerating Situation Prediction over Spatio-temporal Event Streams

Abstract

This paper presents a generic solution to the spatiotemporal prediction problem provided for the DEBS Grand Challenge 2018. Our solution employs an efficient multi-dimensional index to store the training and historical dataset. With the arrival of new tasks of events, we query our indexing structure to determine the closest points of interests. Based on these points, we select the ones with the highest overall score and predict the destination and time of the vessel in question. Our solution does not rely on existing machine learning techniques and provides a novel view of the prediction problem in the streaming settings. Hence, the prediction is not just based on the recent data, but on all the useful historical dataset.