Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust
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arXiv
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| Format: | Preprint |
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2024
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| author | Küsters, Aaron van der Aalst, Wil M. P. |
| author_facet | Küsters, Aaron van der Aalst, Wil M. P. |
| contents | The most commonly used open-source process mining software tools today are ProM and PM4Py, written in Java and Python, respectively. Such high-level, often interpreted, programming languages trade off performance with memory safety and ease-of-use. In contrast, traditional compiled languages, like C or C++, can achieve top performance but often suffer from instability related to unsafe memory management. Lately, Rust emerged as a highly performant, compiled programming language with inherent memory safety. In this paper, we describe our approach to developing a shared process mining library in Rust with bindings to both Java and Python, allowing full integration into the existing ecosystems, like ProM and PM4Py. By facilitating interoperability, our methodology enables researchers or industry to develop novel algorithms in Rust once and make them accessible to the entire community while also achieving superior performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14149 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust Küsters, Aaron van der Aalst, Wil M. P. Software Engineering The most commonly used open-source process mining software tools today are ProM and PM4Py, written in Java and Python, respectively. Such high-level, often interpreted, programming languages trade off performance with memory safety and ease-of-use. In contrast, traditional compiled languages, like C or C++, can achieve top performance but often suffer from instability related to unsafe memory management. Lately, Rust emerged as a highly performant, compiled programming language with inherent memory safety. In this paper, we describe our approach to developing a shared process mining library in Rust with bindings to both Java and Python, allowing full integration into the existing ecosystems, like ProM and PM4Py. By facilitating interoperability, our methodology enables researchers or industry to develop novel algorithms in Rust once and make them accessible to the entire community while also achieving superior performance. |
| title | Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2401.14149 |