Detection of instances of complex structured patterns in large graphically structured databases is a key task in many applications of data mining in areas as diverse as intelligence analysis, fraud detection, biological structure determination, social network analysis, and viral marketing. Successful pattern detection depends on many factors including the size and structure of the database and of the patterns, the completeness of the available data and patterns, and most important, how the data are divided between analysts performing pattern detection. A combinatorial model based on the metaphor of recognizing and classifying jigsaw puzzles is used to study this problem. Experimental results using this model that yield insights into the effect of various parameters are presented. Alternative data organization strategies are developed, presented, and analyzed. A key result is that the likelihood of puzzle recognition — i.e., pattern detection — depends primarily on the ability to group related data elements in a manner that enables them to be examined by a single analyst or group of analysts.