Efficient and Effective Query Answering for Trajectory Cuboids

  • Elio Masciari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7022)


Trajectory data streams are huge amounts of data pertaining to time and position of moving objects generated by different sources continuously using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams poses interesting challenges both for their proper definition and acquisition, thus making the mining process harder than for classical point data. In this paper, we address the problem of trajectory data streams On Line Analytical Processing, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to make the querying step quite effective. We performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.


Prime Number Trajectory Data Trajectory Length Query Answering Measure Approach Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Elio Masciari
    • 1
  1. 1.ICAR-CNRInstitute for the High Performance Computing of Italian National Research CouncilItaly

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