Contents

3. Creating, storing and exploring relational data-nodes in DB (from literature)

3.1. Use-case description

Sub-System: python3 (workflow environment: i.e. orange3)

This use case describes how to create relational data-nodes (e.g. any empirical or theoretical equations reported in literature) in the database, store the data and metadata, explore committed relational data, and retrieve them.

Section

Comment

Use Case Name

Creating, storing and exploring relational data-nodes in DB

Scope

Loading equations and related metadata in the database

Level

Top-Level data submission

Primary Actor

Researchers who wants to calibrate a DEM model (a master or Ph.D. students who has done literature review on useful relations which can be utilized in any calibration steps)

Stakeholders and Interests

Researchers who explore the results of the DEM simulations or develop calibration workflows, or experimentalists who conduct tests for calibration of DEM models

Preconditions

The required metadata about the equation is available (e.g. the author(s), DOI of the article, the relational equation, etc.)

Success Guarantee

Accessing and committing data to the database is completed without error. Data-node is available in the database.

Main Success Scenario

Meta data file is created successfully

Extensions

-

Special Requirements

none

Technology and Data Variations List

-

Frequency of Occurrence

Whenever an input from or comparison with literature data is necessary in the calibration process.

Miscellaneous

-

3.2. Workflow

This feature is a python3 package developed by Richard Amering, as a part of his bachelor project. This package includes several modules, such as relationalData and numericalData, along with their specific metadata classes.

It also includes a high-level database interface to store and retrieve datanodes to and from a file system. The dataFactory module make it possible to create both types of data nodes from either python-functions or numeric collections.

For instance, one can create relational data node from an equation/correlation, given in literature:

from math import pi, log as ln

def CantorCompactionEqu6(phi, phi_0, phi_max, alpha, b, k, Z_0):
    P_star = (
        - b*phi/(2*pi) * (Z_0 + k*(phi-phi_0)**alpha) *
        ln((phi_max-phi)/(phi_max-phi_0)))
    return P_star

from pkg import DataFactory
equation_one = DataFactory.from_relation(CantorCompactionEqu6)
print(equation_one.metadata)
#or update data:
equation_two = DataFactory.from_relation(
    CantorCompactionEqu6,
    name="equ2", creator="Richard",
    author="Cantor", doi="10.1103/PhysRevLett.124.208003")