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

Published: 25 June 2018 Publication History

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.

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

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    cover image ACM Conferences
    DEBS '18: Proceedings of the 12th ACM International Conference on Distributed and Event-based Systems
    June 2018
    289 pages
    ISBN:9781450357821
    DOI:10.1145/3210284
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 25 June 2018

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    Author Tags

    1. Data Stream
    2. Multidimensional Indexing
    3. Prediction

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    • Short-paper
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    • Refereed limited

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    DEBS '18

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    DEBS '18 Paper Acceptance Rate 12 of 31 submissions, 39%;
    Overall Acceptance Rate 145 of 583 submissions, 25%

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