Discovering duplicate tasks in transition systems for the simplification of process models

This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Towards an Entropy-based Analysis of Log Variability

Rules, decisions, and workflows are intertwined components depicting the overall process. So far imperative workflow modelling languages have played the major role for the description and analysis of business processes. Despite their undoubted efficacy in representing sequential executions, they hide circumstantial information leading to the enactment of activities, and obscure the rationale behind the verification of requirements, dependencies, and goals. This workshop aimed at providing a platform for the discussion and introduction of new ideas related to the development of a holistic approach that encompasses all those aspects. The objective was to extend the reach of the business process management audience towards the decisions and rules community and increase the integration between different imperative, declarative and hybrid modelling perspectives. Out of the high-quality submitted manuscripts, three papers were accepted for publication, with an acceptance rate of 50%. They contributed to foster a fruitful discussion among the participants about the respective impact and the interplay of decision perspective and the process perspective

Työkalu joka ryhmittelee vikoja testi logeissa käyttäen merkkijono-samanlaisuus-algoritmeja

This work presents a novel concept of categorising failures within test logs using string similarity algorithms. The concept was implemented in the form of a tool that went through three major iterations to its final version. These iterations are the following: 1) utilising two state-of-the-art log parsing algorithms, 2) manual log parsing of the Pytest testing framework, and 3) parsing of .xml files produced by the Pytest testing framework. The unstructured test logs were automatically converted into a structured format using the three approaches. Then, structured data was compared using five different string similarity algorithms, Sequence Matcher, Jaccard index, Jaro-Winkler distance, cosine similarity and Levenshtein ratio, to form the clusters. The results from each approach were implemented and validated across three different data sets. The concept was validated by implementing an open-sourced Test Failure Analysis (TFA) tool. The validation phase revealed the best implementation approach (approach 3) and the best string similarity algorithm for this task (cosine similarity). Lastly, the tool was deployed into an open-source project’s CI pipeline. Results of this integration, application and usage are reported. The achieved tool significantly reduces software engineers’ manual work and error-prone work by utilising cosine similarity as a similarity score to form clusters of failures