Decision-making can be fostered by knowledge extraction methods such as that of Formal Concept Analysis (FCA). On top of that, information is not available as a whole at all times in certain contexts, such as when it is distributed, and consulting all of it would be too timeconsuming. However, there only exists one algorithm for concept lattice batch computation that does not require full knowledge of the entire set of attributes. But batch algorithms are not best suited for stream processing. For that reason, in this article, we present an incremental algorithm for computing a concept lattice coming from an arbitrarily distributed formal context. And finally, we compare its complexity with that of the existing distributed algorithm.