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Associative Pattern Matching and Inference Using Associative Graph Data Structures

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

Inference on the still growing amount of data is a challenging problem that must be solved to operate efficiently and mine Big Data sources successfully and in a sensible time. This paper introduces a new inference method based on associative recalling of data and their relationships stored in associative graph data structures (AGDS) used for pattern matching for given criteria. These structures represent a richer set of relationships than popular tabular structures do because of their natural limitations. They recall relationships and related data faster than the time-consuming search algorithms based on many loops and conditions on tabular structures. It explains why brain processes trigger information so quickly, outperforming solution based on the Turing Machine and contemporary fast processors. The presented associative inference can work in constant time thanks to the specific data organization, access and direct representation of more relationships in AGDS structures than in the commonly used tabular structures.

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Acknowledgments

This work was supported by AGH 11.11.120.612.

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Authors and Affiliations

  1. AGH University of Science and Technology, Mickiewicza Av. 30, 30-059, Krakow, Poland

    Adrian Horzyk

  2. Warsaw University of Technology, Koszykowa 75, 00-662, Warsaw, Poland

    Agata Czajkowska

Authors
  1. Adrian Horzyk
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  2. Agata Czajkowska
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Corresponding author

Correspondence to Adrian Horzyk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Horzyk, A., Czajkowska, A. (2019). Associative Pattern Matching and Inference Using Associative Graph Data Structures. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_34

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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