Metaquery (also known as metapattern) is a datamining tool useful for learning rules involving more than one relation in the database. A metaquery is a template, or a second-order proposition in a language L, that describes the type of pattern to be discovered. This tool has already been successfully applied to several real-world applications. In this paper we advance the state of the art in metaqueries research in several ways. First, we analyze the related computational problem and classify it as NP-hard, with a tractable subset that is quite immediately evident. Second, we argue that the notion of support for meta-queries, where support is intuitively some indication to the relevance of the rules to be discovered , is not adequately deened in the literature , and propose our own deenition. Third, we propose some eecient algorithms for computing support and present preliminary experimental results that indicate that our algorithms are indeed quite useful.