MOTIVATION: Standardization and semantic alignment have been considered one of the major challenges for data integration in clinical research. The inclusion of the CDISC SDTM clinical data standard into the tranSMART i2b2 via a guiding master ontology tree positively impacts and supports the efficacy of data sharing, visualization and exploration across datasets. RESULTS: We present here a schema for the organization of SDTM variables into the tranSMART i2b2 tree along with a script and test dataset to exemplify the mapping strategy. The eTRIKS master tree concept is demonstrated by making use of fictitious data generated for four patients, including 16 SDTM clinical domains. We describe how the usage of correct visit names and data labels can help to integrate multiple readouts per patient and avoid ETL crashes when running a tranSMART loading routine. AVAILABILITY AND IMPLEMENTATION: The eTRIKS Master Tree package and test datasets are publicly available at https://doi.org/10.5281/zenodo.1009098 and a functional demo installation at https://public.etriks.org/transmart/datasetExplorer/ under eTRIKS-Master Tree branch, where the discussed examples can be visualized.

This paper investigates under which conditions information can be reliably shared and consensus can be solved in unknown and anonymous message-passing networks that suffer from crash-failures. We provide algorithms to emulate registers and solve consensus under different synchrony assumptions. For this, we introduce a novel pseudo leader-election approach which allows a leader-based consensus implementation without breaking symmetry.

We study the problem of consensus in the general omission failure model, i.e., in systems where processes can crash and omit messages while sending or receiving. This failure model is motivated from a smart card-based security framework in which certain security problems can be reduced to consensus in that model. We propose an algorithm that solves consensus based on very weak timing assumptions. More precisely, we show that consensus is solvable using an eventual bisource and a majority of fault-free processes. An eventual bisource is a fault-free process that can eventually communicate with all other processes in a timely manner. In contrast to previous work, we use timing assumptions directly in the algorithm and do not employ the notion of a failure detector. We argue that this is helpful in reducing the message complexity, a critical aspect of algorithms which run on smart cards.

At the heart of distributed computing lies the fundamental result that the level of agreement that can be obtained in an asynchronous shared memory model where t processes can crash is exactly t+1. In other words, an adversary that can crash any subset of size at most t can prevent the processes from agreeing on t values. But what about the rest (22n − n) adversaries that might crash certain combination of processes and not others?