Towards Automated Management and Analysis of Heterogeneous Data Within Cannabinoids Domain

TL;DR

This paper proposes a framework and prototype for automated management and analysis of highly heterogeneous data across the entire cannabinoids research domain, aiming to improve data integration and analysis quality.

Contribution

It introduces a comprehensive framework and prototype for integrating diverse data sources in cannabinoids research, addressing the limitations of domain-specific approaches.

Findings

Prototype of a cannabinoids data platform implemented

Framework enhances data integration across disciplines

Improves analysis accuracy and comprehensiveness

Abstract

Cannabinoid research requires the cooperation of experts from various field biochemistry and chemistry to psychological and social sciences. The data that have to be managed and analysed are highly heterogeneous, especially because they are provided by a very diverse range of sources. A number of approaches focused on data collection and the corresponding analysis, restricting the scope to a sub-domain. Our goal is to elaborate a solution that would allow for automated management and analysis of heterogeneous data within the complete cannabinoids domain. The corresponding integration of diverse data sources would increase the quality and preciseness of the analysis. In this paper, we introduce the core ideas of the proposed framework as well as present the implemented prototype of a cannabinoids data platform.