This repository contains the code and data in order to reproduce the results, including figures, of the article: Decentralised Data Quality Control in Ground Truth Production for Autonomic Decisions, by P. Gkikopoulos, J. Spillner and V. Schiavoni, accepted for publication at IEEE TPDS - Transactions on Parallel and Distributed Systems, 2022. In particular, it contains a reference implementation for the Data-Centric Consensus (DCC) protocol that aims to raise confidence in data-driven decision making as well as data-centric AI.
The code is maintained through Git by Zurich University of Applied Sciences and University of Neuchâtel, with future improvements to the voting/DCC algorithm expected, while the snapshot relevant for the article is also long-term archived on Zenodo.
All results are contained in the following two directories:
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Performance evaluation: Includes the prototype DCC tool and associated documentation used in our performance evaluation and reference to a test dataset that can be used to measure performance.
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Error injection experiment: Includes a standalone script that compares 3 voting algorithms in different error injection configurations. A plotting script used to plot the results and documentation is included.