Taking a Steppe Towards Optimizing Note-Taking Software by Creation of a Note Classification Algorithm

  • Daniela Zieba
  • Wren Jenkins
  • Michael Galloway
  • Jean-Luc Houle
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)


Note-taking software often far surpasses its paper-and-pencil counterpart when measured in metrics such as availability and reliability. However, there is ample opportunity relating to the analysis and organization of notes in structures often called folders, notebooks, or projects within various software. ShovelWare is a project designed for an ongoing field research project analyzing the Bronze and Iron Ages of Mongolia. Accessible through a web interface and cross-platform mobile application, it is a replacement for manual data collection on paper and excessive, error-ridden input into digital spreadsheets. We propose a machine learning algorithm that classifies notes using a variety of metrics, sorting them into graph structures to provide initial insights into the similarity of field notes. As a result, ShovelWare will allow archaeologists to more quickly and cleanly view and share their data. The algorithm, as well as the note-taking structure, are planned with hopes of scalability and applicability into more disciplines.


Note-taking software Archaeology-specific software Machine learning-based classification Data management software Digital recording system 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daniela Zieba
    • 1
  • Wren Jenkins
    • 1
  • Michael Galloway
    • 1
  • Jean-Luc Houle
    • 1
  1. 1.Western Kentucky UniversityBowling GreenUSA

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