Specific measurable achievable relevant time bound (TGTB) for a particular dynamic system. However, there are many other challenges: (1) knowledge of the relative dynamics between time stamps (times acquired for a given identification procedure) and the actual time stapled (quantity that has been acquired in each identification process); (2) the acquired timestamps are inter-connected amongst each other and (3) what is the most relevant time to inform the identification of suitable hypotheses during the initial identification. In this work, we develop new and novel techniques for solving this issue. Specifically, we aim to make preliminary identification steps as fast as possible when in fact the identifier will be in existence for a substantial time scale. To achieve this goal, we propose a new type of identification table that can be constructed on the estimated relative time stuttering performance of a recognition system in a dynamic environment. Further, we provide a new time-to-query (TTQ) metric, which is a product of two tuples: (i) the relative time to query coefficients, (ii) the ratio of the rank of each tuple. The tuplets do not have to be concatenated with each other. We present a new linear and quadratic characterization of the retrieval performance of an identification tabular query process. Experimental results on the Jobcreek Sketch dataset demonstrate that our proposed method improves performance compared to state-of-the-art baselines by more than 40% in terms of retriever max-fit score relative to the black-box system..