PLM Migration in the Era of Big Data and IoT: Analysis of Information System and Data Topology

Abstract

As product lifecycle management is starting to be a standard information system in most companies, a data manipulation issue starts to emerge. Indeed, the information contained in a system often needs to be accessible or even transferred to another one to exploit a new software's functionalities. With the Big Data era, new methods of analysis start to emerge, and the graph visualisation of data enables a better human understanding of the data itself. We propose to address the PLM migration issues with a new approach based on analysis of information system and data topology. The data is passed onto a graph and bundles are made thanks to clustering algorithms; this bundling enables a better understanding of the data and ease migration. However, PLM has links between links which complicate the transition to graphs. Finally, the described use-case proves that data bundling eases the data migration and prevents some usual pitfalls and delays.

Enhancing Data Management Strategies with a Hybrid Layering Framework in Assessing Data Validation and High Availability Sustainability

“…Many recent studies have utilized data migration techniques, such as big bang data migration [33][34][35], trickle data migration [33,36,37], and zero-downtime data migration [21,38,39]. While trickle data migration allows for manageable increments [36] of utilizing the database to improve real-time stream processing without an automated migration process, the big bang migration method [34] involves migrating all data at once, which requires automated migration, testing, and validation. The zero-downtime strategy is a method that integrates the principles of continuous delivery with the objectives of high availability and data consistency [39].…”