A live graphical user interface for the dyClamp sketch
A tool for recovering and replaying missed events in an event-driven system, ensuring accurate recalculations without relying on traditional databases.
This introductory project demonstrates the importance of verifying data consistency prior to using any ML approach. In this example, we show how to measure possible correlations between the features of the dataset and the corresponding targets.
This program takes in a JSON-like file which is a representation of a bus company database and finds all the mistakes in the data.
Distributed system project implementing a two-phase commit protocol with coordinators and participants. I handled failure cases, ensuring data consistency. The coordinator sends commit requests, and participants vote. Based on votes, the coordinator commits or aborts transactions. I simulated failures and used Python for implementation.
A Python package for diagnostic assessment of data consistency in molecular datasets
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