Heavyweight Pattern Mining in Attributed Flow Graphs
Carolina Simoes Gomes, José Nelson Amaral, Jörg Sander, Joran Siu, Li Ding · 2014
This paper defines a new problem - heavyweight pattern mining in attributed flow graphs. The problem can be described as the discovery of patterns in flow graphs that have sets of attributes associated with their nodes. A connection between nodes is represented as a directed edge. The amount of load that goes through a path between nodes, or the frequency of transmission of such load between nodes, is represented as edge weights. A heavyweight pattern is a sub-set of attributes, found in a dataset of attributed flow graphs, that are connected by edges and have a computed weight higher than an user-defined threshold. A new algorithm called AFG Miner is introduced, the first one to our knowledge that finds heavyweight patterns in a dataset of attributed flow graphs and associates each pattern with its occurrences. The paper also describes a new tool for compiler engineers, HEP Miner, that applies the AFG Miner algorithm to Profile-based Program Analysis modeled as a heavyweight pattern mining problem.