Desire lines in big data : using event data for process discovery and conformance checking

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader IS (Information Systems) and KM (Knowledge Management) communities to look at process-centric knowledge discovery. This paper summarizes the manifesto and is based on a paper with the same title that appeared in the December 2011 issue of SIGKDD Explorations (Volume 13, Issue 2)

Desire lines in big data : using event data for process discovery and conformance checking

Recently, the Task Force on Process Mining released the Process Mining Manifesto. The manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active contributions from end-users, tool vendors, consultants, analysts, and researchers illustrate the growing relevance of process mining as a bridge between data mining and business process modeling. This paper summarizes the manifesto and explains why process mining is a highly relevant, but also very challenging, research area. This way we hope to stimulate the broader IS (Information Systems) and KM (Knowledge Management) communities to look at process-centric knowledge discovery. This paper summarizes the manifesto and is based on a paper with the same title that appeared in the December 2011 issue of SIGKDD Explorations (Volume 13, Issue 2)

A fine grained heuristic to capture web navigation patterns

In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information obtained from web logs is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm’s running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures the distance between the current rule-set and its corresponding maximal set obtained by the BFS algorithm. When the stopping parameter takes the value zero the heuristic corresponds to the BFS algorithm and as the parameter takes values closer to one the number of rules obtained decreases accordingly. Experiments were conducted with both real and synthetic data and the results show that for a given cut-point the number of rules induced increases smoothly with the decrease of the stopping criterion. Therefore, by setting the value of the stopping criterion the analyst can determine the number and quality of rules to be induced; the quality of a rule is measured by both its length and probability