scholarly journals A Discovery Method for Hierarchical Software Execution Behavior Models Based on Components

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yahui Tang ◽  
Tong Li ◽  
Rui Zhu ◽  
Fei Du ◽  
Jishu Wang ◽  
...  
Keyword(s):  
Software Design ◽  
Process Mining ◽  
Real Life ◽  
Event Logs ◽  
Modeling Data ◽  
Public Datasets ◽  
Software Execution ◽  
Discovery Method ◽  
Behavior Models ◽  
Design And Testing

Software is rapidly evolving and operates in a changing environment; therefore, in addition to software design and testing, it is essential to observe and understand the software execution behavior by modeling data recorded during the execution of the software to improve its reliability. The nested call relationship between methods during the execution of software is common, but most process-mining methods are unable to discover them, only generating flat models with low fitness. Meanwhile, it is easy to generate “spaghetti-like” models with low comprehensibility when dealing with complex software execution data. This paper proposes a component-based hierarchical software behavior model discovery method that can discover hierarchical nested call structures during software runtime, improving the fitness of the model; meanwhile, the proposed method partitions the discovery model into several parts by component information to improve the comprehensibility of the model, which can also reflect the interaction behavior within and between components. The proposed approach was implemented in a process mining toolkit. Using real-life software event logs and public datasets, we demonstrated that compared with other advanced process mining techniques, our approach can visualize actual software execution behavior in a more accurate and easy-to-understand way while balancing time performance.

Process Mining of Event Logs: A Case Study Evaluating Internal Control Effectiveness

2019 ◽  
Vol 33 (3) ◽  
pp. 141-156 ◽  
Author(s):  
Tiffany Chiu ◽  
Mieke Jans
Keyword(s):  
Internal Control ◽  
Process Mining ◽  
Real Life ◽  
Potential Control ◽  
Control Effectiveness ◽  
Event Logs ◽  
Event Log ◽  
New Type ◽  
Potential Risks

SYNOPSISThis paper aims at adopting process mining to evaluate the effectiveness of internal control using a real-life event log. Specifically, the evaluation is based on the full population of an event log and it contains four analyses: (1) variant analysis that identifies standard and non-standard variants, (2) segregation of duties analysis that examines whether employees violate segregation of duties controls, (3) personnel analysis that investigates whether employees are involved in multiple potential control violations, and (4) timestamp analysis that detects time-related issues including weekend activities and lengthy process duration. Results from the case study indicate that process mining could assist auditors in identifying audit-relevant issues such as non-standard variants, weekend activities, and personnel who are involved in multiple violations. Process mining enables auditors to detect potential risks, ineffective internal controls, and inefficient processes. Therefore, process mining generates a new type of audit evidence and could revolutionize the current audit procedure.

scholarly journals Analysis of Cargo Release Services with Process Mining

2020 ◽  
Vol 02 (01) ◽  
pp. 1-13
Author(s):  
Sagit Kedem-Yemini
Keyword(s):  
Process Mining ◽  
Physical Map ◽  
Real Life ◽  
Release Process ◽  
Value Analysis ◽  
Process Improvements ◽  
Organizational Challenges ◽  
Event Logs ◽  
Logistics Company ◽  
Actual Movement

Process Mining (PM) uses event logs extracted from process-oriented IS in order to uncover, analyse, diagnose and improve processes. However, the number of studies demonstrating PM applicability is limited, particularly in the field of logistics. This paper presents a methodological framework for a multi-faceted analysis of real-life event logs based on PM and the usefulness of its techniques, combined with traditional IE&M methods, thus offering an innovative approach on multiple levels by combining the use of PM and more traditional methods; using PM to demonstrate the actual movement of goods and generate a physical map of movements inside the warehouse; and enabling continuous tracking. A case-study, implemented on the cargo release process of a large Israeli logistics company, demonstrates this approach. Results reflect a major gap between the actual and the described processes, as an automatic creation of the process from logs shows that 64% of the customers received their goods after 4.5 hours (instead of 90 minutes, as service standard requires). Practical implications include detailed steps and a recommendation for additional analyses. Research value analysis shows that PM techniques constitute an ideal means to tackle organizational challenges by reflecting real-time situations, suggesting process improvements and creating companywide process awareness.

Cross-Organisational Process Mining in Cloud Environments

2018 ◽  
Vol 17 (02) ◽  
pp. 1850014 ◽  
Author(s):  
Mario Luca Bernardi ◽  
Marta Cimitile ◽  
Francesco Mercaldo
Keyword(s):  
Cloud Computing ◽  
Distributed Systems ◽  
Resource Management ◽  
Business Processes ◽  
Process Mining ◽  
Process Analysis ◽  
Real Life ◽  
Event Logs ◽  
Private And Public ◽  
Organisational Process

Cloud computing market is continually growing in the last years and becoming a new opportunity for business for private and public organisations. The diffusion of multi-tenants distributed systems accessible by clouds leads to the birth of some cross-organisational environments, increasing the organisation efficiency, promoting the business dynamism and reducing the costs. In spite of these advantages, this new business model drives the interest of researchers and practitioners through new critical issues. First of all, the multi-tenant distributed systems need new techniques to improve the traditional resource management distribution along the different tenants. Secondly, new approaches to the process analysis and monitoring analysed since cross-organisational environments allow various organisations to execute the same process in different variants. Hence, information about how each process variant characterised can be collected by the system and stored as process logs. The usefulness of such logs is twofold: these logs can be analysed using some process mining techniques to understand and improve the business processes and can be used to find better resource management and scalability. This paper proposes a cloud computing multi-tenancy architecture to support cross-organisational process executions and improve resource management distribution. Moreover, the approach supports the systematic extraction/composition of distributed data from the system event logs that are assumed to carry information of each process variant. To this aim, the approach also integrates an online process mining technique for the runtime extraction of business rules from event logs. Declarative processes are used to represent process variants running on the analysed infrastructure as they are particularly suited to represent the business process in a context characterised by low predictability and high variability. In this work, we also present a case study where the proposed architecture is implemented and applied to the execution of a real-life process of online products selling.

scholarly journals Discovering high-level BPMN process models from event data

2019 ◽  
Vol 25 (5) ◽  
pp. 995-1019 ◽  
Author(s):  
Anna Kalenkova ◽  
Andrea Burattin ◽  
Massimiliano de Leoni ◽  
Wil van der Aalst ◽  
Alessandro Sperduti
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Real Life ◽  
Process Models ◽  
Holistic View ◽  
Life Event ◽  
Content Type ◽  
Event Logs ◽  
Wide Range ◽  
High Level

Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

scholarly journals Full Support for Efficiently Mining Multi-Perspective Declarative Constraints from Process Logs

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 29 ◽  
Author(s):  
Christian Sturm ◽  
Myriel Fichtner ◽  
Stefan Schönig
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Distributed Processing ◽  
Real Life ◽  
Research Problem ◽  
Process Models ◽  
Mining Technique ◽  
Event Logs ◽  
Satisfactory State ◽  
Mining Algorithms

Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time.

scholarly journals FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5172
Author(s):  
Yuying Dong ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Skin Lesion ◽  
Real Life ◽  
Good Deal ◽  
Experimental Tests ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Attention Network ◽  
Effective Feedback ◽  
Public Datasets ◽  
Learning Architectures

Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

scholarly journals Cross-Organization Emergency Response Process Mining: An Approach Based on Petri Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Cong Liu ◽  
Huiling Li ◽  
Qingtian Zeng ◽  
Ting Lu ◽  
Caihong Li
Keyword(s):  
Petri Nets ◽  
Emergency Response ◽  
Resource Sharing ◽  
Process Mining ◽  
Process Models ◽  
Response Process ◽  
Extensive Experience ◽  
Event Logs ◽  
Collaboration Patterns ◽  
Sharing Patterns

To support effective emergency disposal, organizations need to collaborate with each other to complete the emergency mission that cannot be handled by a single organization. In general, emergency disposal that involves multiple organizations is typically organized as a group of interactive processes, known as cross-organization emergency response processes (CERPs). The construction of CERPs is a time-consuming and error-prone task that requires practitioners to have extensive experience and business background. Process mining aims to construct process models by analyzing event logs. However, existing process mining techniques cannot be applied directly to discover CERPs since we have to consider the complexity of various collaborations among different organizations, e.g., message exchange and resource sharing patterns. To tackle this challenge, a CERP model mining method is proposed in this paper. More specifically, we first extend classical Petri nets with resource and message attributes, known as resource and message aware Petri nets (RMPNs). Then, intra-organization emergency response process (IERP) models that are represented as RMPNs are discovered from emergency drilling event logs. Next, collaboration patterns among emergency organizations are formally defined and discovered. Finally, CERP models are obtained by merging IERP models and collaboration patterns. Through comparative experimental evaluation using the fire emergency drilling event log, we illustrate that the proposed approach facilitates the discovery of high-quality CERP models than existing state-of-the-art approaches.

scholarly journals Business process mining

2017 ◽  
Vol 01 (01) ◽  
pp. 1630004 ◽  
Author(s):  
Asef Pourmasoumi ◽  
Ebrahim Bagheri
Keyword(s):  
Process Mining ◽  
Status Quo ◽  
Process Models ◽  
Added Value ◽  
Conformance Checking ◽  
Event Logs ◽  
Organizational Information ◽  
Organization Process ◽  
The Status ◽  
Business Process Mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

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