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Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments

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Database and Expert Systems Applications (DEXA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12923))

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Abstract

Big moving objects arise as a novel class of big data objects in emerging environments. Here, the main problems are the following: (i) tracking, which represents the baseline operation for a plethora of higher-level functionalities, such as detection, classification, and so forth; (ii) analysis, which meaningfully marries with big data analytics scenarios. In line with these goals, in this paper we propose a novel family of scan matching algorithms based on registration, which are enhanced by using a genetic pre-alignment phase based on a novel metrics, fist, and, second, performing a finer alignment using a deterministic approach. Our experimental assessment and analysis confirms the benefits deriving from the proposed novel family of such algorithms.

A. Cuzzocrea—This research has been made in the context of the Excellence Chair in Computer Engineering – Big Data Management and Analytics at LORIA, Nancy, France.

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Author information

Authors and Affiliations

  1. iDEA Lab, University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. LORIA, Nancy, France

    Alfredo Cuzzocrea

  3. Faculty of Engineering, Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Rijeka, Croatia

    Kristijan Lenac

  4. University of Trieste, Trieste, Italy

    Enzo Mumolo

Authors
  1. Alfredo Cuzzocrea
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  2. Kristijan Lenac
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  3. Enzo Mumolo
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Corresponding author

Correspondence to Enzo Mumolo .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  3. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  4. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Cuzzocrea, A., Lenac, K., Mumolo, E. (2021). Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2021. Lecture Notes in Computer Science(), vol 12923. Springer, Cham. https://doi.org/10.1007/978-3-030-86472-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-86472-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86471-2

  • Online ISBN: 978-3-030-86472-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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