Regions In a Linked Dataset For Change Detection

TL;DR

This paper proposes a method to prioritize regions within linked datasets to enable more efficient and frequent change detection, helping applications stay updated with minimal computational resources.

Contribution

It introduces a region prioritization approach for change detection in linked datasets, optimizing accuracy and resource use during dataset evolution.

Findings

Enhanced change detection efficiency

Supports near real-time data updates

Optimizes resource utilization

Abstract

Linked Datasets (LDs) are constantly evolving and the applications using a Linked Dataset (LD) may face several issues such as outdated data or broken interlinks due to evolution of the dataset. To overcome these issues, the detection of changes in LDs during their evolution has proven crucial. As LDs evolve frequently, the change detection during the evolution should also be done at frequent intervals. However, due to limitation of available computational resources such as capacity to fetch data from LD and time to detect changes, the frequent change detection may not be possible with existing change detection techniques. This research proposes to explore the notion of prioritization of regions (subsets) in LDs for change detection with the aim of achieving optimal accuracy and efficient use of available computational resources. This will facilitate the detection of changes in an evolving LD at frequent intervals and will allow the applications to update their data closest to real-time data.