DEKOMPOSISI PROSES BISNIS UNTUK OPTIMASI PROSES MINING

Proses mining atau proses discovery adalah metode yang secara otomatis dapat menemukan dan merekonstruksi alur kerja proses bisnis dari event log. Proses mining penting untuk menemukan aktivitas-aktivitas yang dieksekusi bersamaan karena banyak dari aktivitas tersebut terkandung dalam proses bisnis. Karena proses discovery mendapat perhatian di antara para peneliti dan praktisi, maka kualitas proses model yang dihasilkan akan diperlukan. Model proses bisnis biasanya mengandung input, output, dan relasi, baik sequence maupun paralel. Namun, sistem informasi enterprise yang ada sekarang sudah kompleks dan disusun oleh jumlah komponen besar yang biasanya diimplementasikan di lingkungan sebuah perusahaan. Oleh karena itu, proses mining digunakan untuk men-discover model struktural untuk workflow yang kompleks dari event log yang berasal dari berbagai departemen yang ada di dalam sebuah perusahaan. Dalam skripsi ini, diusulkan metode untuk menemukan dan menggabungkan proses model untuk sistem informasi yang kompleks dengan menggunakan top-to-down proses mining berdasarkan perbaikan petri nets dari multi-source event log serta algoritma proses mining yang populer, yaitu algoritma Heuristics Miner, berdasarkan modifikasi interval waktu. Dengan menggunakan informasi waktu untuk aktivitas yang dilakukan memungkinkan algoritma untuk menghasilkan model alur kerja yang lebih baik. Algoritma yang diusulkan dapat secara efektif membedakan relasi single choice XOR, conditional OR dan parallel AND. Interval ambang batas (threshold) ditentukan berdasarkan rata-rata ukuran dependency graph. Hasil eksperimen menunjukkan bahwa algoritma yang diusulkan dapat menemukan proses bisnis kompleks dari sebuah perusahaan yang berasal dari berbagai departemen dan proses model yang dibentuk oleh parallel AND dan conditional OR serta mengevaluasi metode lain dari algoritma proses discovery, seperti algoritma Heuristics Miner, algoritma Process Model Discovery Based on Activity Lifespan dan algoritma Heuristics Miner for Time Intervals. Setelah itu, kami menunjukkan perbandingan dari kualitas fitness masing-masing algoritma, validitas dari proses model dengan algoritma yang diusulkan, kelengkapan dari proses model dengan algoritma yang diusulkan dan optimasi waktu dan biaya produksi dengan menggunakan pemrograman non-linear. ========================================================================================================= Process Mining or Process Discovery is a method to automatically discover and reconstruct the workflow of a business process from logs of activities. Process mining for discovering concurrent activities is important since there are many of them contained in business processes. Since researchers and practitioners are giving attention to the process discovery, then the quality of the discovered process models is required. Business process model contains input, output, and relation (sequence and parallel traces). However today’s enterprise information systems are complex and composed of a large number of components, which are usually implemented on an organization. So, process mining is used to discover the structural model for a complex workflow from multi-source event log collected from distributed department. In this undergraduate thesis book, we propose a method to obtain and to merge process model for complex workflow systems using top-to-down process mining based on refinement of petri nets from multi-source event log and a modification to one of process mining algorithms, Heuristics Miner, to Modified Time-Based Heuristics Miner. We propose modification method because using time-based information for the activities in event log enable the algorithm to produce better process models. The proposed algorithm can effectively distinguish single choice XOR, conditional OR and parallel AND. The threshold intervals are determined based on average dependency measure in dependency graph. The experimental results show that the proposed algorithm can discover the complex business process from an organizational that is collected from distributed department and the concurrent business processes formed by parallel AND and conditional OR and evaluate other methods for process discovery techniques, such as original Heuristics Miner algorithm, Process Model Discovery Based on Activity Lifespan and Heuristics Miner for Time Intervals. After that, we show the evaluation of quality fitness, the validity, the completeness and time-cost optimization using non-linear programming

Process Mining Handbook

This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools Aarhus, Denmark, October 24-26, 2005

This booklet contains the proceedings of the Sixth Workshop on Practical Use of Coloured Petri Nets and the CPN Tools, October 24-26, 2005. The workshop is organised by the CPN group at the Department of Computer Science, University of Aarhus, Denmark. The papers are also available in electronic form via the web pages: http://www.daimi.au.dk/CPnets/workshop0