Joel Ribeiro

Process Mining Manifesto

Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes.

A Recommender System for Process Discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.

Quantitative Analysis of Resource-Constrained Business Processes

To address the need for evaluation techniques for complex business processes, also known as workflows, this paper proposes an approach based on generalized stochastic Petri nets (GSPNs). We review ten related approaches published in the last fifteen years and compare them to our approach using a wide range of criteria. On the basis of this evaluation, we observe that the newly proposed approach provides results that are at least as good as those from the most accepted alternatives and holds a number of additional advantages, such as modeling simplicity, improved precision, and model reuse for qualitative analyses. The overall approach is formally defined in this paper, along with the definition of several performance metrics. Part of these metrics can be computed analytically, while the remainder can be obtained by simulating the GSPN. Furthermore, a tool has been developed to translate automatically business process execution language processes into GSPNs. Finally, we present a case study in which we applied the proposed approach, colored Petri net tools, and an industrial tool to obtain performance insights into a realistic workflow. The results were highly similar, demonstrating the feasibility and the accuracy of our approach.

Improving Product Quality and Reliability with Customer Experience Data

Advance technology development and wide use of the World Wide Web have made it possible for new product development organizations to access multi-sources of data-related customer complaints. However, the number of customer plaints of highly innovative consumer electronic products is still increasing; that is, product quality and reliability is at risk. This article aims to understand why existing solutions from literature as well as from industry to deal with these increasingly complex multiple data sources are not able to manage product quality and reliability. Three case studies in industry are discussed. On the basis of the case study results, this article also identifies a new research agenda that is needed to improve product quality and reliability under this circumstance. Copyright (c) 2011 John Wiley & Sons, Ltd.

Mining significant change patterns in multidimensional spaces

In this paper, we present a new OLAP Mining method for exploring interesting trend patterns. Our main goal is to mine the most (TOP-K) significant changes in Multidimensional Spaces (MDS) applying a gradient-based cubing strategy. The challenge is then finding maximum gradient regions, which maximises the task of detecting TOP-K gradient cells. Several heuristics are also introduced to prune MDS efficiently. In this paper, we motivate the importance of the proposed model, and present an efficient and effective method to compute it by: evaluating significant changes by means of pushing gradient search into the partitioning process; measuring Gradient Regions (GR) spreadness for data cubing; measuring Periodicity Awareness (PA) of a change, assuring that it is a change pattern and not only an isolated event; devising a Rank Gradient-based Cubing to mine significant change patterns in MDS.

A Method for Assessing Parameter Impact on Control-Flow Discovery Algorithms

Given a log L, a control-flow discovery algorithm f, and a quality metric m, this paper faces the following problem: what are the parameters in f that mostly influence its application in terms of m when applied to L? This paper proposes a method to face this problem, based on sensitivity analysis, a theory which has been successfully applied in other areas. Clearly, a satisfactory solution to this problem will be crucial to bridge the gap between process discovery algorithms and final users. Additionally, recommendation techniques and meta-techniques like determining the representational bias of an algorithm may benefit from solutions to the problem considered in this paper. The method has been evaluated over a set of logs and two different miners: the inductive miner and the flexible heuristic miner, and the experimental results witness the applicability of the general framework described in this paper.

Detecting abnormal patterns in call graphs based on the aggregation of relevant vertex measures

Graphs are a very important abstraction to model complex structures and respective interactions, with a broad range of applications including web analysis, telecommunications, chemical informatics and bioinformatics. In this work we are interested in the application of graph mining to identify abnormal behavior patterns from telecom Call Detail Records (CDRs). Such behaviors could also be used to model essential business tasks in telecom, for example churning, fraud, or marketing strategies, where the number of customers is typically quite large. Therefore, it is important to rank the most interesting patterns for further analysis. We propose a vertex relevant ranking score as a unified measure for focusing the search of abnormal patterns in weighted call graphs based on CDRs. Classical graph-vertex measures usually expose a quantitative perspective of vertices in telecom call graphs. We aggregate wellknown vertex measures for handling attribute-based information usually provided by CDRs. Experimental evaluation carried out with real data streams, from a local mobile telecom company, showed us the feasibility of the proposed strategy.