Masterarbeit / Bachelorarbeit
A web-based solution for metatda management in Jupyter Notebooks
Research Area
Advisers
Description
Efficient metadata management is crucial for the organization,
retrieval, and analysis of data within Jupyter Notebooks. As data scientists and
researchers increasingly rely on Jupyter Notebooks for their interactive and iterative
data exploration and analysis, the need for a streamlined solution to manage metadata
becomes evident. A robust metadata management system enhances productivity, collaboration,
and reproducibility by systematically organizing information about data sources, analysis
workflows, and results. Current methods for managing metadata in Jupyter Notebooks are
often ad hoc and manually intensive, leading to inconsistencies and inefficiencies. There
is a pressing need for an automated, web-based solution that integrates seamlessly with
Jupyter Notebooks, enabling users to efficiently manage metadata without interrupting
their workflow. This solution should not only support the capture and organization of
metadata but also facilitate easy retrieval, sharing, and updating of this
information.
This thesis aims to develop a web-based software tool,
designed to automate metadata management in Jupyter Notebooks. The user interface of this
web-based system must be intuitive and tailored to support data scientists and researchers
in automatically capturing and managing metadata, guided by insights from previous studies
on metadata management and existing tools in the field. The key features of the solution
needs to include Automated Metadata Capture, User-Friendly Interface, Extensibility,
Persistent and Referencable Metadata. The system needs to automatically extract metadata
from Jupyter Notebooks, including information about data sources, code execution, results,
and workflow context. The user interface that facilitates easy viewing, editing, and
updating of metadata. The system must be easily extendable to accommodate new types of
metadata and integrate with additional tools and workflows. The solution must ensure that
metadata entries are persistable and can be referenced via persistent URLs, supporting
reproducibility and long-term data management needs. The metadata captured from the
Jupyter notebooks is stored as a knowledge graph, enabling querying of the metadata.
The objective of this thesis is to analyze the current state of metadata
management in Jupyter Notebooks, identify existing gaps and requirements, and develop a
comprehensive solution that addresses these needs. This includes designing and
implementing the web-based tool, followed by an experimental evaluation through a pilot
study to demonstrate its effectiveness and usability. By advancing metadata management in
Jupyter Notebooks through this web-based system, this thesis aims to significantly enhance
the efficiency and effectiveness of data science workflows, promoting better data
organization, collaboration, and reproducibility.
