Borja Vázquez-Barreiros

ProDiGen: mining complete, precise and minimal structure process models with a genetic algorithm

Abstract Process discovery techniques automatically extract the real workflow of a process by analyzing the events that are collected and stored in log files. Although in the last years several process discovery algorithms have been presented, none of them guarantees to find complete, precise and simple models for all the given logs. In this paper we address the problem of process discovery through a genetic algorithm with a new fitness function that takes into account both completeness, precision and simplicity. ProDiGen (Process Discovery through a Genetic algorithm) includes new definitions for precision and simplicity, and specific crossover and mutation operators. The proposal has been validated with 39 process models and several noise levels, giving a total of 111 different logs. We have compared our approach with the state of the art algorithms; non-parametric statistical tests show that our algorithm outperforms the other approaches, and that the difference is statistically significant.

Recompiling learning processes from event logs

We extract a process structure from event logs using process mining.We obtain the rules that guide adaptive learning from these logs by means of decision tree learning.We developed an algorithm to recompile the extracted process structure and rules in IMS Learning Design (IMS LD).The proposed framework facilitates the reuse of units of learning from legacy and proprietary e-learning systems. In this paper a novel approach to reuse units of learning (UoLs) - such as courses, seminars, workshops, and so on - is presented. Virtual learning environments (VLEs) do not usually provide the tools to export in a standardized format the designed UoLs, making thus more challenging their reuse in a different platform. Taking into account that many of these VLEs are legacy or proprietary systems, the implementation of a specific software is usually out of place. However, these systems have in common that they record the events of students and teachers during the learning process. The approach presented in this paper makes use of these logs (i) to extract the learning flow structure using process mining, and (ii) to obtain the underlying rules that control the adaptive learning of students by means of decision tree learning. Finally, (iii) the process structure and the adaptive rules are recompiled in IMS Learning Design (IMS LD) - the de facto educational modeling language standard. The three steps of our approach have been validated with UoLs from different domains.

Evaluation of a Data-To-Text System for Verbalizing a Learning Analytics Dashboard

The SoftLearn Activity Reporter is a data‐to‐text service, which automatically generates textual reports about the activity developed by students within the SoftLearn virtual learning environment. In this paper, we describe the conception of the service, its architecture, and its subsequent evaluation by an expert pedagogue, where 20 full reports generated from real data from an undergraduate course supported by the SoftLearn platform were assessed. Results show that the automatically generated reports are a valuable complementary tool for explaining teachers and students the information comprised in a learning analytics dashboard.

Repairing Alignments: Striking the Right Nerve

Process Mining is concerned with the analysis, understanding and improvement of business processes. One of the most important branches of process mining is conformance checking, i.e. assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute conformance statistics. Alignments map elements of an event log onto activities present in a business process model. However, computing them is a combinatorial problem and hence, extremely costly. In this paper we show how to compute an alignment for a given process model, using an existing alignment and an existing process model as a basis. We show that we are able to effectively repair the existing alignment by updating those parts that no longer fit the given process model. Thus, computation time decreases significantly. Moreover, we show that the potential loss of optimality is limited and stays within acceptable bounds.

A Genetic Algorithm for Process Discovery Guided by Completeness, Precision and Simplicity

Several process discovery algorithms have been presented in the last years. These approaches look for complete, precise and simple models. Nevertheless, none of the current proposals obtains a good integration between the three objectives and, therefore, the mined models have differences with the real models. In this paper we present a genetic algorithm (ProDiGen) with a hierarchical fitness function that takes into account completeness, precision and simplicity. Moreover, ProDiGen uses crossover and mutation operators that focus the search on those parts of the model that generate errors during the processing of the log. The proposal has been validated with 21 different logs. Furthermore, we have compared our approach with two of the state of the art algorithms.

Enhancing discovered processes with duplicate tasks

Including duplicate tasks in the mining process is a challenge that hinders the process discovery, as it is also necessary to find out which events of the log belong to which transitions. To face this problem, we propose SLAD (Splitting Labels After Discovery), an algorithm that uses the local information of the log to enhance an already mined model, by performing a local search over the tasks that have more probability to be duplicated in the log. This proposal has been validated with 54 different mined models from three process discovery algorithms, improving the final solution in 45 of the cases. Furthermore, SLAD has been tested in a real scenario.