Papers by Chathura Ekanayake
Lecture Notes in Computer Science, 2018
Ballerina is a new language for solving integration problems. It is based on insights and best pr... more Ballerina is a new language for solving integration problems. It is based on insights and best practices derived from languages like BPEL, BPMN, Go, and Java, but also cloud infrastructure systems like Kubernetes. Integration problems were traditionally addressed by dedicated middleware systems such as enterprise service buses, workflow systems and message brokers. However, such systems lack agility required by current integration scenarios, especially for cloud based deployments. This paper discusses how Ballerina solves this problem by bringing integration features into a general purpose programming language.
School of Information Systems Science Engineering Faculty, 2014
Business process management enables organizations to better understand and continuously improve b... more Business process management enables organizations to better understand and continuously improve business operations. In this context, process models play a key role by providing an abstract, yet precise, representation of business processes, which serves as a basis for communicating, analyzing and implementing business operations. With the increased adoption of process-oriented techniques, the number of process models available within organizations has grown rapidly, especially in the context of large organizations that operate in different locations and serve a multitude of different customers.
Information Systems, 2014
Automated process discovery techniques aim at extracting process models from information system l... more Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models -each one representing a variant of the business process -as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.
Lecture Notes in Computer Science, 2012
Evidence exists that repositories of business process models used in industrial practice contain ... more Evidence exists that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication may stem from the fact that the repository describes variants of the same processes and/or because of copy/pasting activity throughout the lifetime of the repository. Previous work has put forward techniques for identifying duplicate fragments (clones) that can be refactored into shared subprocesses. However, these techniques are limited to finding exact clones. This paper analyzes the problem of approximate clone detection and puts forward two techniques for detecting clusters of approximate clones. Experiments show that the proposed techniques are able to accurately retrieve clusters of approximate clones that originate from copy/pasting followed by independent modifications to the copied fragments.
Lecture Notes in Computer Science, 2013
Changes introduced as a result of publishing processes such as copy-editing and formatting may no... more Changes introduced as a result of publishing processes such as copy-editing and formatting may not be reflected in this document.
Approximate clone detection is the process of identifying similar process fragments in business p... more Approximate clone detection is the process of identifying similar process fragments in business process model collections. The tool presented in this paper can efficiently cluster approximate clones in large process model repositories. Once a repository is clustered, users can filter and browse the clusters using different filtering parameters. Our tool can also visualize clusters in the 2D space, allowing a better understanding of clusters and their member fragments. This demonstration will be useful for researchers and practitioners working on large process model repositories, where process standardization is a critical task for increasing the consistency and reducing the complexity of the repository.
Information Systems, 2015
Empirical evidence shows that repositories of business process models used in industrial practice... more Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This article studies the broader problem of approximate clone detection in process models. The article proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hierarchical Agglomerative Clustering (HAC). The article also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable.
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Papers by Chathura Ekanayake