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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case.

Alessio Capello1, Matteo Fresta1, Francesco Bellotti1

  • 1Department of Electrical, Electronic and Telecommunication Engineering (DITEN), University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy.

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Summary
This summary is machine-generated.

We developed a data toolchain to automate project progress monitoring by simplifying the setup of reporting tools. This system effectively extracts key performance indicators from experimental data, enhancing project management efficiency.

Keywords:
RESTful APIsautomated drivingbig data architecturefield operational testsnon-relational DBproject monitoring and reporting

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Area of Science:

  • Data Science
  • Software Engineering
  • Project Management

Background:

  • Timely project status information is crucial for effective management.
  • Existing frameworks may require extensive customization for progress monitoring.
  • Automated data extraction is essential for handling large volumes of project data.

Purpose of the Study:

  • To develop a data toolchain for automated project progress monitoring.
  • To simplify the setup of reporting tools through configuration files.
  • To ensure automatic extraction of project performance indicators from experimental data.

Main Methods:

  • Extended the Measurify framework for building measurement-rich applications on MongoDB.
  • Utilized JSON configuration files for defining project progress/performance indicators.
  • Focused on automatic data extraction from project experiment data files.

Main Results:

  • Successfully developed a data toolchain supporting automated project progress monitoring.
  • The toolchain simplifies reporting tool setup via editable JSON configuration files.
  • Demonstrated effectiveness in a collaborative research project, identifying 330+ numerical indicators.

Conclusions:

  • The developed data toolchain is effective for preparing periodic progress reports using actual project data.
  • Design choices, including API resource definitions, ensure broad applicability beyond the automotive industry.
  • The toolchain enhances project management by providing timely and data-rich insights into project status.