A Distance-Window Approach for the Continuous Processing of Spatial Data Streams

Salman Ahmed Shaikh, Akiyoshi Matono and Kyoung-Sook Kim
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Salman Ahmed Shaikh: National Institute of Advanced Industrial Science and Technology (AIST), Japan
Akiyoshi Matono: National Institute of Advanced Industrial Science and Technology (AIST), Japan
Kyoung-Sook Kim: National Institute of Advanced Industrial Science and Technology (AIST), Japan

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2020, vol. 11, issue 2, 16-30

Abstract: Real-time and continuous processing of citywide spatial data is an essential requirement of smart cities to guarantee the delivery of basic life necessities to its residents and to maintain law and order. To support real-time continuous processing of data streams, continuous queries (CQs) are used. CQs utilize windows to split the unbounded data streams into finite sets or windows. Existing stream processing engines either support time-based or count-based windows. However, these are not much useful for the spatial streams containing the trajectories of moving objects. Hence, this paper presents a distance-window based approach for the processing of spatial data streams, where the unbounded streams can be split with respect to the trajectory length. Since the window operation involves repeated computation, this work presents two incremental distance-based window approaches to avoid the repetition. A detailed experimental evaluation is presented to prove the effectiveness of the proposed incremental distance-based windows.

Date: 2020
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