Pattern-based change detection (PBCD) describes a class of change detection algorithms for evolving data. Contrary to conventional solutions, PBCD seeks changes exhibited by the patterns over time and therefore works on an abstract form of the data, which prevents the search for changes on the raw data. Moreover, PBCD provides arguments on the validity of the results because patterns mirror changes occurred with any form of evidence. However, the existing solutions differ on data representation, mining algorithm and change identification strategy, which we can deem as main modules of a general architecture, so that any PBCD task could be designed by accommodating custom implementations for those modules. This is what we propose in this paper through jKarma, a highly-modular framework for designing and performing PBCD
