SEDIMARK Logo

SEDIMARK PoC Videos

PoC #01 - Participant Onboarding

In this video, we demonstrate the powerful features of the IOTA Identity framework applied to the SEDIMARK participant onboarding scenario.  It showcases how offering tokenization and access control are enabled in a DLT-enabled ecosystem exchanging assets stored in an IPFS infrastructure.

PoC #02 - Data Processing Pipeline

Mage.ai helps at orchestrating data pipelines throughout the entire data lifecycle, from preprocessing to model creation and prediction. By utilizing data managed by EGM (including weather and water dam information), it supports the development and maintenance of the four pipelines demonstrated in the video. Leveraging Mage.ai’s capabilities enable the creation of reliable workflows that can be executed on demand.

PoC #03 - Offering Lifecycle

This scenario demonstrates the establishment and management of the Offering lifecycle within the SEDIMARK ecosystem.
The Offering serves as the means of describing Assets available for exchange and used by the Catalogue. During the lifetime of an Offering, an Offering will undergo changes based on the Providers preferences or circumstances, and therefore need to be considered.
The scenario enables the publication of Offerings from Providers to the Marketplace. It also enables Consumers to search and discover Offerings through a distributed Catalogue. This is done through a defined set of interactions between modules within the SEDIMARK toolbox and the Baseline Infrastructure.
The lifecycle is split into 2 main phases, Registration, and Discovery, and breaks down into six stages: Creation, Registration, Population, Discovery, Update, Withdrawal.

The Ontology that Offerings are modelled from can be found here: https://w3id.org/sedimark/ontology/

PoC #04 - Asset Transfer

Overview and Demonstration of Technologies for Trustworthy Data Sharing in SEDIMARK Marketplace. In this video, we present a detailed technical demonstration of the technologies enabling secure and trustworthy data sharing among participants in the SEDIMARK Marketplace. Using Eclipse DataSpace components and based on MVD from the Eclipse Foundation, we showcase how these innovative solutions facilitate efficient and secure collaboration and information exchange.

PoC #05 - AI Pipeline

In this video, we demonstrate an AI pipeline using an enhanced deep learning model, the improved version of Crossformer, applied to the SEDIMARK use case for multivariate time-series forecasting. The pipeline's process, including data loading, multivariate selection, model training, and inference, is also covered.

Crossformer

PoC #06 - Marketplace User Inteface

Description 06

Coming soon!

crossmenu