Time series are commonly used to store temporal data, e.g., sensor measurements. However, when it comes to complex analytics and learning tasks, these measurements have to be combined with structural context data. Temporal graphs, connecting multiple time- series, have proven to be very suitable to organize such data and ultimately empower analytic algorithms. Computationally intensive tasks often need to be distributed and parallelized among different workers. For tasks that cannot be split into independent parts, several workers have to concurrently read and update these shared temporal graphs. This leads to inconsistency risks, especially in the case of frequent updates. Distributed locks can mitigate these risks but come with a very high-performance cost. In this paper, we present a lock-free approach allowing to concurrently modify temporal graphs. Our approach is based on a composition operator able to do online reconciliation of concurrent modifications of temporal graphs. We evaluate the efficiency and scalability of our approach compared to lock-based approaches
