A trending topic in Semantic Web research deals with the processing of queries over Linked Open Data (LOD). As has been shown in literature, the loose nature of the "web of data" and data sources within can be accounted for by employing federated query processing strategies. This approach, however, is all the more dependent on both up-to-date statistical summaries (data statistics) of the sources in use and accurate and precise estimation of cardinalities and selectivities. In general, federated data sources are to be seen as black-boxes w.r.t. data statistics, as no interchange of such information can be expected. Because of this, it is possible for individual data statistics to become obsolete, if the corresponding source is subjected to data changes cumulating over time. In this thesis an adaptive system is being proposed, that complements a given RDF-based query federator. Through observation and analysis of the error of the cardinality estimation of incoming queries, it tries to infer the obsolescence of individual data statistics, triggering updates of data statistics found to be obsolete. An evaluation of the system shows, that the approach to this solution is plausible. Yet, in practice no satisfying results could be acquired, that would prove a true practicality. Still, parts of the system proposed may be re-used for related tasks that could be more promising.