CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities

An intentional process model is known to analyze processes deeply and provide recommendations for the upcoming processes. Nevertheless, the discovery of intentions is a difficult task because the intentions are not recorded in the event log, but they encourage the executable activities in the event log. Map Miner is the latest algorithm to depict the intentional process model. A disadvantage of this algorithm is the inability to determine   strategies   that   contain   same   activities   with   the different sequence with other strategies. This disadvantage leads failure on the intentional process model. This research proposes an  algorithm for  discovering  an intentional  process  model  by considering the sequence of activities and CHMM (Coupled Hidden Markov Model). The probabilities and states of CHMM are utilized for the formation of the intentional process model. The experiment shows that the proposed algorithm with considering the sequence of activities gets an appropriate intentional process model. It also demonstrates that an obtained intentional  process  model  using  proposed  algorithm  gets  the better  validity  than  an  intentional  process  model  using  Map Miner Method

Determining Process Model Using Time-Based Process Mining and Control-Flow Pattern

Determining right model of business process from event log is the purpose of process discovery. However some problems i.e the inability to discover OR, noise and event log incompleteness are emmerged while determining right model of business process. First, OR relation is often discovered as AND relation. Second, noise problem is occured when there are truncated and low frequency traces in event log. Thus control-flow pattern is used to solve issues of same noise relation frequency hence it discovers relation based on transaction function of activity. Consequently, it can refine non noise relation in business process model. Third, incompleteness leads to incorrect discovery of parallel process model; therefore we used Timed-based Process Mining which utilized non-linear dependence to solve the incompleteness. Finally this paper proposed combination of Timed-based Process Mining and control-flow pattern to discover OR and handle same frequency noise and incompleteness. From the experiment in section 3, this proposed method manages to get right process model from event log

Process Discovery untuk Streaming Event Log menggunakan Model Markov Tersembunyi

Process discovery adalah teknik penggalian model proses dari rangkaian aktivitas yang tercatat dalam event log. Saat ini, sistem informasi menghasilkan streaming event log dimana Online Heuristic Miner adalah algoritma process discovery yang mampu menghasilkan model proses dari streaming event log. Algoritma Online Heuristic Miner memiliki kelemahan yaitu ketidakmampuan mengatasi incomplete trace. Incomplete trace adalah rangkaian aktivitas pada event log yang terpotong di bagian awal ataupun di bagian akhir. Incomplete trace mengakibatkan proses tidak dapat ditampilkan secara utuh dalam model proses. Algoritma yang memanfaatkan Model Markov Tersembunyi digunakan untuk membentuk model proses yang dapat menangani incomplete trace. Algoritma yang memanfaatkan Model Markov Tersembunyi terdiri atas gabungan dari metode pembentukan model proses serta metode yang dimodifikasi. Metode yang dimodifikasi adalah metode Baum- Welch, Backward serta Viterbi. Metode Backward dan Viterbi yang dimodifikasi digunakan untuk memperbaiki incomplete trace sedangkan metode Baum-Welch yang dimodifikasi dan metode pembentukan model proses digunakan untuk membangun model proses dari Model Markov Tersembunyi. Hasil uji coba menunjukkan bahwa dengan adanya perbaikan incomplete trace, nilai kualitas dari sisi fitness, presisi, generalisasi, dan simplicity model proses dari algoritma yang memanfaatkan Model Markov Tersembunyi lebih tinggi dibandingkan model proses dari algoritma Online Heuristic Miner

Time-based α+ miner for modelling business processes using temporal pattern

Business processes are implemented in an organization. When a business process is run, it generates event log. One type of event log is double timestamp event log. Double timestamp has the start and complete time of each activity executed in the business process and has a close relationship with temporal pattern. In this paper, seven types of temporal pattern between activities were presented as extended version of relations used in the double timestamp event log. Since the event log was not always executed in sequential way, therefore using temporal pattern, event log was divided into several small groups to mine the business process both sequential and parallel. Both temporal pattern and Time-based α+ Miner algorithm were used to mine process model, determined sequential and parallel relations and then evaluated the process model using fitness value. This paper was focused on the advantages of temporal pattern implemented in Time-based α+ Miner algorithm to mine business process. The results also clearly stated that the proposed method could present better result rather than that of original α+ Miner algorithm

Valuable Business Knowledge Asset Discovery by Processing Unstructured Data.

Modern organizations are challenged to enact a digital transformation and improve their competitiveness while contributing to the ninth Sustainable Development Goal (SGD), “Build resilient infrastructure, promote sustainable industrialization and foster innovation”. The discovery of hidden process data’s knowledge assets may help to digitalize processes. Working on a valuable knowledge asset discovery process, we found a major challenge in that organizational data and knowledge are likely to be unstructured and undigitized, constraining the power of today’s process mining methodologies (PMM). Whereas it has been proved in digitally mature companies, the scope of PMM becomes wider with the complement proposed in this paper, embracing organizations in the process of improving their digital maturity based on available data. We propose the C4PM method, which integrates agile principles, systems thinking and natural language processing techniques to analyze the behavioral patterns of organizational semi-structured or unstructured data from a holistic perspective to discover valuable hidden information and uncover the related knowledge assets aligned with the organization strategic or business goals. Those assets are the key to pointing out potential processes susceptible to be handled using PMM, empowering a sustainable organizational digital transformation. A case study analysis from a dataset containing information on employees’ emails in a multinational company was conducted.post-print5352 K

Monitoring applications with process mining

Esta pesquisa apresenta um novo método para alavancar técnicas de Process Mining com o objetivo de monitorizar e suportar aplicações de Sistemas de Informação. Combinando as metodologias de Revisão Sistemática da Literatura e de Pesquisa em Design Science para abordar os objetivos da investigação. A revisão da literatura foi conduzida para explorar a investigação existente de Process Mining para monitorizar e suportar aplicações. A revisão de literatura seguiu rigorosos critérios de inclusão e exclusão, selecionando um conjunto de estudos que serviram como base de conhecimento, respondendo às questões de investigação colocadas. Foi descrito o potencial dos atuais métodos, as suas aplicações, limitações e dimensões inexploradas. Com base nesta revisão, a metodologia Design Science Research foi utilizada para desenvolver um novo método que descreve uma abordagem estruturada e sistemática para a aplicação de técnicas de Process Mining, especificamente adaptadas para monitorizar e suportar aplicações complexas de Sistemas de Informação. Enfatizando a utilidade prática, o método descreve etapas detalhadas, componentes e diretrizes para uma implementação eficaz. Posteriormente, foi avaliado e validado através de um cenário real de caso de uso, afirmando sua eficácia e potencial impacto em aplicações reais. O processo de avaliação concentrou-se na capacidade de o método identificar ineficiências de processos e fornecer suporte para a tomada de decisões em aplicações de Sistemas de Informação. As conclusões derivadas deste estudo contribuem para a aplicabilidade de Process Mining, introduzindo um método que visa melhorar as capacidades de monitorização e suporte de aplicações de Sistemas de Informação. Esta investigação reforça a relevância prática e o potencial transformador da integração do Process Mining no domínio da gestão de Sistemas de Informação e estabelece as bases para futuras investigações neste campo.This research presents a novel method aimed at leveraging Process Mining techniques for monitoring and supporting Information Systems applications. Combining a Systematic Literature Review and the Design Science Research Methodology to address the research objectives comprehensively. The Systematic Literature Review was conducted to explore the existing landscape of Process Mining for application's monitor and support. The review process followed rigorous inclusion and exclusion criteria, selecting a collection of pertinent studies that served as a foundational knowledge base. Through this review, key insights were reported regarding the current methodologies, their applications, limitations, and the unexplored dimensions within the field. From these insights, the Design Science Research Methodology was then employed to conceptualize and develop a new method. This method outlines a structured and systematic approach to applying Process Mining techniques specifically tailored for monitoring and supporting complex Information Systems applications. Emphasizing practical utility, the method encompasses detailed steps, components, and guidelines for effective implementation. Subsequently, the proposed method was evaluated and validated through a real-world use-case scenario, affirming its efficacy and potential impact in actual application environments. The evaluation process focused on assessing the method's ability to derive actionable insights, identify process inefficiencies, and provide support for decision-making within Information Systems applications. The findings derived from this study contribute to the field of Process Mining by introducing a tailored methodology aimed at enhancing the monitoring and support capabilities of Information Systems applications. This research reinforces the practical relevance and potential transformative impact of integrating Process Mining into the domain of Information Systems management and lays the groundwork for future advancements in this field

Perbaikan Proses Bisnis yang Terpotong dalam Kasus yang Mengandung Aktivitas Tidak Terlihat dan Pilihan Tidak Bebas pada Terminal Petikemas

Algoritma-algoritma pembentukan model proses akan berjalan maksimal apabila log data yang diolah adalah log data yang lengkap. Pada kenyataannya, terdapat proses-proses yang tidak lengkap diakibatkan pengambilan data pada interval waktu tertentu. Untuk memberikan hasil maksimal bagi algoritma, maka diperlukan perbaikan terhadap proses yang terpotong dengan menjadikan model proses sebagai acuan. Model proses deklaratif terdiri dari peraturan-peraturan yang menjelaskan relasi proses secara fleksibel untuk mempermudah analisa dan modifikasi. Akan tetapi, model proses deklaratif tidak menggambarkan control-flow pattern secara mendetail, sehingga relasi proses tidak tergambar secara langsung. MinerFul adalah algoritma yang mengkonversi model deklaratif menjadi model proses imperatif, model yang menggambarkan control-flow pattern. Akan tetapi, MinerFul tidak dapat menggambarkan relasi non-free choice pada kondisi proses yang melibatkan invisible task. Kemudian, model proses imperatif tidak memperhatikan data tambahan yang disebut atribut, yang digunakan untuk penentuan aktivitas pengganti pada relasi pilihan dalam perbaikan proses terpotong. Penelitian ini membentuk model proses imperatif tree yang mampu menggambarkan non-free choice dan invisible task dengan cara menggabungkan control-flow pattern yang terbentuk dalam Linear Temporal Logic (LTL). LTL dipilih karena ia merupakan bahasa formal dalam penggambarakn relasi komponen yang berkaitan dengan waktu dan LTL dapat dikonversi menjadi model tree yang merupakan representasi model proses imperatif. Kemudian, model proses imperatif akan diberi tambahan atribut dari data tambahan. Atribut adalah data selain aktivitas yang ditambahkan di log data, yang dalam penelitian ini adalah detail lampiran aktivitas. Data ini akan ditambahkan pada aktivitas di relasi pilihan model proses imperatif. Kemudian, proses terpotong akan diperbaiki dengan menggolongkan proses terpotong dan anomali dan pemberian aktivitas pengganti berdasarkan urutan node dengan kondisi khusus. Kondisi khusus adalah aktivitas pengganti pada relasi pilihan yang dipilih adalah aktivitas yang memiliki atribut yang sesuai dengan atribut pada proses terpotong. Kemampuan penggambaran non-free choice dan invisible task pada model proses imperatif menghasilkan nilai presisi dan simplicity lebih tinggi dari hasil MinerFul, dengan rata-rata nilai presisi adalah 0,861 dan nilai simplicity adalah 0,878. Kemudian, hasil perbaikan proses yang terpotong dengan memperhatikan atribut menunjukkan bahwa akurasi lebih tinggi dibandingkan algoritma Heuristic Linear Approach terutama dalam proses yang terpotong di awal, dimana rata-rata akurasi yang didapat adalah 82,16%. ======================================================================================================== Process discovery algorithms run optimally if an event log is a complete log. In fact, there is a truncated process due to data retrieval at certain time intervals. To provide maximum results for the algorithms, recovering the truncated process is necessary. A declarative process model consists of several rules that explain the relations of process flexibly. It leads to the ease in analyzing or modifying the process. However, a declarative model does not describe control-flow patterns in detail, so the relations of process are not drawn directly. MinerFul can convert a declarative model to an imperative model, a model that describes control-flow patterns. The disadvantage of MinerFul is an inability to describe non-free choice relations in the processes involving invisible tasks. Furthermore, the imperative model does not consider additional data called attributes, which are used for the determination of replacement activities of choice relations in the truncated process. This research forms an imperative model, in the form model tree, that involves non-free choice and invisible tasks by combining control-flow pattern in Linear Temporal Logic (LTL). LTL is chosen because it is a formal language for describing the relation of components related to time and it can be converted into a tree model as the imperative model. Afterwards, the imperative model will be given additional attributes. Attribute is data, other than activity, added in the event log. In this research, attributes are detail attachments of activities in the event log. This data will be added to the activity in the choice relation of the imperative model. Then, the truncated processes will be corrected by classifying the truncated and anomalous and then providing replacement activity based on the order of nodes in the preorder and reverse preorder with a special condition. A special condition is a chosen activity on the choice relation is an activity that has an attribute that matches the attribute of the truncated process. Non-free choice and invisible task capability by proposed method obtains higher precision and simplicity values than MinerFul results, with an average precision value of 0.861 and a simplicity value of 0.878. Then, the result of recovering truncated processes with respect to the attribute by proposed method shows higher accuracy than the result of Heuristic Linear Approach, especially in the truncated process at the beginning. The average accuracy of the recovered results by proposed method is 82.16%

Optimasi Kinerja Pada Cross-Organizational Business Process Model Informatika Institut Teknologi Sepuluh Nopember Surabaya

Optimasi cross-organizational business process adalah salah satu masalah yang harus dipecahkan. Untuk mengoptimasi kinerja dalam cross-organizational business process yang pertama dilakukan adalah melakukan process discovery terhadap model proses bisnis dari event log. Banyak algoritma process discovery yang telah diterapkan seperti Alpha, Alpha ++ dan Heuristic Miner, tetapi tidak dapat men-discover parallel OR. Oleh kerena itu Tugas Akhir ini memodifikasi Heuristic Miner dengan menggunakan interval threshold untuk men-discover parallel XOR, AND, dan OR. Interval threshold ditentukan berdasarkan rata-rata dari positive dependency measure dalam dependency matriks. Setelah mendapatkan model dari cross-organizational business process event log, kemudian dilakukan optimasi kinerja dengan mendapatkan durasi minimum proses bisnis dan biaya tambahan yang minimum. CPM crashing project adalah salah astu metode yang digunakan untuk time–cost optimization. Tetapi CPM crashing project memerlukan beberapa data untuk melakukan percepatan durasi proses bisnis tetapi pada realitanya banyak data yang berbentuk single timestamp event log yang tidak memiliki viii data yang dibutuhkan untuk CPM crashing project. Oleh karena itu dalam Tugas akhir ini menggunakan perhitungan rata-rata durasi eksekusi dan biaya setiap aktivitas dari setiap case untuk menentukan data optimasi yang digunakan untuk CPM crashing project. Kemudian linear programming digunakan untuk mendapatkan durasi minimum dan biaya tambahan minimum dari proses bisnis. Hasil uji coba menunjukkan bahwa modified Heuristic Miner dapat discover OR split dan join, dan linear programming dengan data optimasi yang telah dihitung sebelumnya dapat melakukan optimasi terhadap cross-organizational business process dengan mendapatkan minimum durasi dan minimum biaya tambahan yang digunakan. ======================================================================================================== In cross-organizational business process model performance optimization is one of the problem which should be solve. To optimize the cross-organizational business process model the first thing to do is discover the business process from event log. Many algorithms have been employed for process discovery, such as Alpha, Alpha ++ and Heuristic Miner but they cannot discover processes containing parallel OR. Therefor in this undergraduate thesis represents the modified Heuristic Miner which utilizes the threshold intervals to discover parallel XOR, AND, and OR. The threshold intervals are determined based on average positive dependency measure in dependency matrix. After getting the model of cross-organizational business process event log then optimize the model to get the minimum duration of process and minimum additional cost which is needed. CPM crashing project is one of method to solve the time–cost optimization. But CPM crashing project need some data to speeding up the process business. But in reality there are a lot of data which is represent using single timestamp event log which does not provide data to do CPM crashing project. Therefor this undergraduate thesis represents a method to get data which is use to crashing project. The data is got from averaging time execution x of each activity in case in event log. Then to crashing the project this undergraduate thesis use linear programming to get minimum duration and minimum additional cost. The results show that the modified Heuristic Miner can discover OR split and join, and using linear programming use the data which is calculate, this undergraduate thesis can optimize the performance of cross-organizational business process by getting minimum makspan and minimum additional cost

Process Discovery Untuk Streaming Event Log Menggunakan Model Markov Tersembunyi

Process discovery adalah teknik penggalian model proses dari rangkaian aktivitas yang tercatat dalam event log. Saat ini, sistem informasi menghasilkan streaming event log dimana event log dicatat sesuai waktu proses yang terjadi. Online Heuristic Miner adalah algoritma process discovery yang mampu menghasilkan model proses dari streaming event log. Kelemahan dari algoritma Online Heuristic Miner adalah ketidakmampuan mengatasi incomplete trace pada streaming event log. Incomplete trace adalah rangkaian aktivitas pada event log yang terpotong di bagian awal ataupun di bagian akhir. Incomplete trace mengakibatkan proses secara utuh tidak dapat ditampilkan dalam model proses sehingga incomplete trace disebut sebagai noise. Algoritma yang memanfaatkan Model Markov Tersembunyi digunakan untuk membentuk model proses yang dapat menangani incomplete trace pada streaming event log. Model Markov Tersembunyi dipilih karena model ini banyak digunakan untuk meramalkan data sehingga berguna dalam memperbaiki incomplete trace. Algoritma yang memanfaatkan Model Markov Tersembunyi tersebut terdiri atas gabungan dari metode pembentukan model proses serta metode yang dimodifikasi. Metode yang dimodifikasi adalah metode Baum- Welch, Backward serta Viterbi, dimana pemodifikasian diperuntukkan untuk mengelompokkan observer pada Model Markov Tersembunyi dan menanggulangi aktivitas yang belum tercatat dalam Model Markov Tersembunyi. Uji coba dilakukan dengan menggunakan tiga periode process discovery, yaitu setiap 5 detik, setiap 10 detik dan setiap 15 detik. Hasil uji coba menunjukkan bahwa algoritma yang memanfaatkan Model Markov Tersembunyi mampu memperbaiki incomplete trace sehingga tidak ada aktivitas yang hilang dalam model proses. Hasil uji coba juga menunjukkan kualitas model proses dari algoritma yang memanfaatkan Model Markov Tersembunyi lebih baik dibandingkan kualitas model proses dari algoritma Online Heuristic Miner. ========== Process discovery is a technique for obtaining process model from sequences of activities recorded in the event log. Nowadays, information systems produce a streaming event log wherein it is recorded according to the execution time. Online Heuristic Miner is an algorithm of process discovery which capable of obtaining process models from the streaming event log. Disadvantage using an Online Heuristic Miner algorithm is the inability to overcome the incomplete trace on the streaming event log. The incomplete trace is a truncated trace, either clipped off at the beginning or clipped off at the end. The incomplete trace make a whole process cannot be displayed in the process model, so incomplete trace is noise in process discovery. An algorithm utilizing Hidden Markov Model is used to obtain a model process that can handle incomplete traces on a streaming event log. Hidden Markov Model is chosen because this model is widely used to predict the data which is useful in recovering incomplete trace. The algorithm utilizing Hidden Markov Model consists of a combination of methods of model process and modified methods. The modified methods are Baum- Welch, Backward and Viterbi methods, where the modification is intended to classify observer on Hidden Markov Model and combat activities that have not been recorded in the Hidden Markov Model. The experiments are using three period of discovery process, i.e. every 5 seconds, every 10 seconds and every 15 seconds. Experimental results show that the algorithm utilizing Hidden Markov Model is capable of recovering incomplete trace, so all activities are displayed in the process model. The experiment results also showed the quality of the process models obtaining by algorithm utilizing Hidden Markov Model is better than those obtaining algorithms Online Heuristic Miner

Algoritma Time-based Alpha Miner untuk Memodelkan Proses Bisnis dan Pengoptimasian Menggunakan Sistem Manufaktur Fleksibel di Terminal Petikemas

Manajemen proses bisnis dilakukan untuk mendapatkan hasil yang diinginkan dalam waktu dan biaya yang optimum. Waktu yang optimum dapat dicapai dengan menambah sumber daya. Sedangkan, biaya yang optimum dapat dicapai dengan mengurangi sumber daya. Oleh karena itu, diperlukan sistem yang dapat mengoptimasi proses bisnis. Namun, optimasi hanya dapat bekerja apabila ada data model proses bisnis. Sehingga juga diperlukan sistem yang dapat mengenali proses bisnis. Proses bisnis bisa diperoleh dengan menggunakan teknik process discovery yang bekerja dengan menggali relasi dari data log. Relasi tersebut adalah sequence, paralel (XOR, OR, dan AND). Process discovery yang sudah ada dapat menggali relasi paralel (XOR, OR, dan AND), sequence, perulangan (loop), non-free choice, dan invisible task. Selain itu, sebagian besar process discovery yang sudah ada juga menggunakan single timestamp untuk menemukan model proses. Di dalam penelitian yang telah dilakukan oleh peneliti-peneliti sebelumnya, aktivitas dapat diparalelkan dengan cara mengetahui hubungan resiprokal (misal: aktivitas A berelasi sequence kepada aktivitas B dan sebaliknya) antaraktivitas untuk menggali relasi paralel dalam data log. Misal trace AB, BA untuk paralel antara aktivitas A dan B. Namun, tidak dikaitkan dengan kondisi yang dibutuhkan untuk memparalelkan kedua aktivitas di data log. Untuk memparalelkan sebuah proses bisnis, aktivitas yang independen harus diidentifikasi terlebih dahulu, seperti aktivitas manakah dari proses bisnis yang dapat dilakukan bersamaan. Tingkat paralelisme tertinggi dicapai jika jumlah aktivitas yang diidentifikasi sebagai independen dapat dimaksimalkan. Secara umum, identifikasi ini didasarkan pada waktu dan tempat eksekusi aktivitas, aktivitas dapat di paralelisasi jika aktivitas pada entitas simulasi yang sama dijalankan dalam urutan timestamp. Untuk meningkatkan tingkat paralelisme, kami mengusulkan sebuah pendekatan baru yang mengidentifikasi kriteria independensi lain: Jika dua aktivitas pada entitas simulasi yang sama mengakses item data yang sama dengan cara yang berbeda, mereka dapat dieksekusi secara paralel. Pada Tesis ini akan diusulkan kondisi yang diperlukan untuk memparalelkan dua kegiatan di dalam data log dan mengembangkan sistem yang dapat memodelkan relasi proses bisnis secara otomatis dan dapat mengoptimasi biaya dan waktu sekaligus. Optimasi biaya dan waktu dilakukan dengan mempertimbangkan sumber daya (machine) dengan menggunakan Sistem Manufaktur Fleksibel (FMS) untuk memperoleh jumlah total mesin dapat berganti fungsi dalam satu hari dan satu bulan serta Goal Programming yang digunakan untuk mengoptimasi biaya dan waktu dari setiap departemen sehingga menghasilkan nilai waktu dan biaya yang optimum. Hasil eksperimen menunjukkan bahwa algoritma Modified Time-based Alpha Miner dapat menemukan model proses dengan benar serta relasi paralel AND, OR dan XOR, sementara algoritma original Alpha hanya dapat mengkategorikan relasi paralel menjadi AND dan XOR. Setelah model proses ditemukan, dengan menggunakan Sistem Manufaktur Fleksibel, Departemen Behandle dan Karantina dapat dieksekusi paralel dengan merubah fungsi mesin RTGC menjadi HT Truck. Dengan menggunakan kakas bantu LEKIN dan metode First Come First Serve (FCFS), hasil penjadwalan di Terminal Petikemas dapat diketahui berdasarkan jenis containernya. Lalu, hasil optimasi dengan Goal programming adalah waktu maksimum setiap aktivitas yang diminimalkan menjadi rata-rata durasi eksekusi per aktivitas dan biaya yang dapat dihemat dari waktu maksimum tersebut. ====================================================================================== Bussiness process management is used to produce product within optimized time and cost. The optimized time can be achieved by adding resource; whereas, the optimized cost can be achieved by decreasing resource. Therefore, it is needed a system to optimize business process. However, the optimization only works if business process model available. Hence, it is also needed a system to discover business process. Business process can be obtained by using process discovery technique that works by mining relation from event log. The relation which can be obtained is sequence and parallel (XOR, OR, and AND). Existing process discovery can discover the parallel relations (XOR, OR, and AND), sequence, loop, non-free choice, and invisible task. In addition, most existing discovery processes use single timestamp to discover the process model. In previous study done by researchers, activity can be paralleled by knowing the reciprocal relationship (e.g. activity A has sequence relation to activity B and vice versa) to mine the parallel relation in the event log, e.g. trace AB, BA to parallel between activity A and B. However, it does not have necessary condition to parallelize the two activities in the event log. To parallelize simulations of business process, independent activities have to be identified, which can be executed concurrently. The highest level of parallelism is achieved if the number of activities identified as independent is maximized. Traditionally, this identification is based on time and location of activities, only allowing parallelization if activities on the same simulation entity are executed in timestamp order. To increase the level of parallelism, we propose a novel approach investigating another criterion for independence: If two activities on the same simulation entity do not access the same data items in a conflicting manner, they can as well be executed in parallel. In this Thesis research, we will propose the necessary conditions to parallelize two activities in the event log and develop a system that can model business processes automatically and can optimize the cost and time at once. Cost and time optimization is done by considering the resources (machine) by using Flexible Manufacturing System (FMS) to obtain the total number of machines can change the function in one day and one month and Goal Programming which is used to optimize cost and time of each department so as to generate value optimum time and cost. The experimental results show that Modified Time-based Alpha Miner algorithm can find process models correctly and gateway parallel AND, OR and XOR, while the original Alpha Miner algorithm can only categorize parallel relations into AND and XOR. Once the modeling process is found, using the Flexible Manufacturing System, the Behandle and Quarantine departments can be executed parallel by changing the function of the RTGC engine to HT Truck. By using LEKIN tools and First Come First Serve (FCFS) method, the scheduling result in container terminal can be known based on container type. Then, the optimization result with Goal programming is the maximum time each activity is minimized to the average duration of execution per activity and the cost can be saved from that maximum time