Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

Metric-based Evaluation of Implemented Software Architectures

PurposeProcess mining has emerged in the last decade as one of the most promising tools to discover and understand the actual execution of processes. This paper addresses the application of process mining techniques to analyze the performance of automatic guided vehicles (AGVs) in one of the Body in White circuits of the factory that Stellantis has in Vigo, Spain.Design/methodology/approachStandard process mining discovery and conformance algorithms are applied to analyze the different AGV execution paths, their lead times, main sources and identify any unexpected potential situations, such as unexpected paths or loops.FindingsResults show that this method provides very useful insights which are not evident for logistics technicians. Even with such automated devices, where the room for decreased efficiency can be apparently small, process mining shows there are cases where unexpected situations occur, leading to an increase in circuit times and different variants for the same route, which pave the road for an actual improvement in performance and efficiency.Originality/valueThis paper provides evidence of the usefulness of applying process mining in manufacturing processes. Practical applications of process mining have traditionally been focused on processes related to services and management, such as order to cash and purchase to pay in enterprise resource planning software. Despite its potential for use in industrial manufacturing, such contributions are scarce in the current state of the art and, as far as we are aware of, do not fully justify its application.

Care pathways are widely used as evidence-based clinical governance tools to enhance the quality of care of groups of patients with a specific clinical problem and optimize the use of resources. However, it is often the case that there are differences between the recommended care pathway and the actual clinical practice. Recently, Process Mining (PM) techniques, a family of data-driven techniques from computer science that uses logs (execution traces) of a system to reason about its underlying process, have been applied in the healthcare context to map and analyze real-world practice patterns. In particular, PM helps to discover and analyze the sequence of activities, to highlight variances and possible sub-optimal management of the clinical paths in order to improve care quality and reduce the inefficient allocation of resources. Using the breast cancer pathway as a case study example, this study aims to describe the application of PM to administrative healthcare data of public hospitals of the Tuscany Region (Italy) and to offer insights about strengths and limitations in data management, information creation, and interpretation to support decision-making. The study revealed variations in the management of breast cancer care pathways across different public healthcare providers and with respect to the recommended guidelines. Key findings include instances of service duplication, delays, and bottlenecks, particularly in the diagnostic phase. The analysis also highlighted variations in healthcare costs, driven by differences in the frequency and types of diagnostic exams or visits performed. The findings have practical implications for enhancing the efficiency and quality of breast cancer care and provide a practical example of how the methodology can be applied to other healthcare contexts for similar benefits.

Business process management is a popular approach that has caught the attention of several researchers over the years. BPM has been extended to Business Process Intelligence in order to add more intelligence to managing business processes. Process Mining is one of the emerging areas of Business Process Intelligence, which aims to automatically discover, verify and improve business processes. Nowadays, the emergence of smart environment. The data that occurs in this environment are more varied and are kind of unstructured data. In this sense, applying process mining, to analyze processes in a smart context is a challenging task, since the process mining techniques relies on a structured event log. In this paper, we present a comparative study of existing approaches on applying process mining techniques in smart BPM, as a first step towards developing a new approach to discover and analyze business processes from sensor data perspective.KeywordsProcess Mining (PM)Smart environmentsSensor logSmart BPM

Introduced in the early stages of software development, the Charmy framework assists the software architect in making and evaluating architectural choices. Rarely, the software architecture of a system can be established once and forever. Most likely poorly defined and understood architectural constraints and requirements force the software architect to accept ambiguities and move forward to the construction of a suboptimal software architecture. Charmy aims to provide an easy and practical tool for supporting the iterative modeling and evaluation of software architectures. From an UML-based architectural design, an executable prototype is automatically created. Charmy simulation and model checking features help in understanding the functioning of the system and discovering potential inconsistencies of the design. When a satisfactory and stable software architecture is reached, Java code conforming to structural software architecture constraints is automatically generated through suitable transformations. The overall approach is tool supported.

With the technology emerging more and more possible applications of process mining in healthcare become apparent. In most cases the goal of applying process mining to the healthcare domain is to find out what actually happened and to deliver a concise assessment of the organizational reality by mining the event logs of health information systems. To develop medical guidelines or patient pathways considering economic aspects and quality of care, a comparative analysis of different existing approaches is useful (e.g. how different hospitals execute the same process in different ways). This work discusses how to use existing process mining techniques for comparative analysis of healthcare processes and presents an approach based on the L* life-cycle model.

Process mining techniques and applications – A systematic mapping study

This research focuses on analyzing and improving maintenance process by exploring novel applications of process mining and predictive analytics. We analyze the software maintenance process by applying process mining on software repositories, and address the identified inefficiencies using predictive analytics. To drive our research, we engage with practitioners from large, global IT companies and emphasize on the practical usability of the proposed solution approaches, which are evaluated by conducting a series of case studies on open source and commercial projects.

In healthcare facilities, processes are not always carried out under the expected methods. The variation in practice leads to lesser quality treatments and greater costs. Within a single visit, patients are now likely to interact with multiple departments, healthcare providers, and Health Information Systems (HIS). Because of these events, information is frequently dispersed, not normalized, and incoherent, creating several barriers to overview processes and audit their quality. Process mining can be a useful tool for getting over some of these obstacles. We designed a procedure to automatically apply process mining techniques using the HL7 standard, which is used for exchanging information between HIS, as a source of event logs. Our work provides a way for pooling HL7 messages from a unified repository of a healthcare institution and provides a pipeline to apply process mining methods to create insights relative to the healthcare processes that are implemented. We show a few diagrams to demonstrate the tool’s potential as a process formalization and analysis tool. We concluded that using HL7 messages as a proxy for processes that involve several HIS is a way to easily provide process mining capabilities to an organization.

The need for software architecture evaluation is based on the realization that software development, like all engineering disciplines, is a process of continuous modeling and refinement. Detecting architectural problems before the bulk of development work is done allows re‐architecting activities to take place in due time, without having to rework what has already been done. At the same time, tuning activities allow software performance to be enhanced and maintained during the software lifetime. When dealing with product families, architectural evaluations have an even more crucial role: the evaluations are targeted to a set of common products. We have tried different approaches to software assessments with our mobile phone software, an embedded real‐time software platform, which must support an increasingly large number of different product variants. In this paper, we present a case study and discuss the experiences gained with three different assessment techniques that we have worked on during the past five years. The assessment techniques presented include scenario‐based software architecture assessment, software performance assessment and experience‐based assessment. The various evaluation techniques are complementary and, when used together, constitute a tool which a software architect must be aware of in order to maintain and evolve a large software intensive system. Copyright © 2006 John Wiley & Sons, Ltd.

Process mining consists of mining event logs generated from business process execution supported by Information Systems (IS). Process mining of software repositories has diverse applications because vast data is generated during Software Development Life Cycle (SDLC) and archived in IS such as Version Control System (VCS), Peer Code Review (PCR) System, Issue Tracking System (ITS), and mail archives. There is need to explore its applications on different repositories to aid managers in process management. We conduct two phase surveys and interviews with managers in a large, global, IT company. The first survey and in-person interviews identify the process challenges encountered by them that can be addressed by novel applications of process mining. We filter, group and abstract responses formulating 30 generic problem statements. On the basis of process mining type, we classify identified problems to eight categories such as control analysis, organizational analysis, conformance analysis, and preventive analysis. The second survey asks distinct participants the importance of solving identified problems. We calculate proposed Net Importance Metric (NIM) using 1262 ratings from 43 participants. Combined analysis of NIM and first survey responses reveals that the problems mentioned by few practitioners in first survey are considered important by majority in the second survey. We elaborate on possible solutions and challenges for most frequent and important problems. We believe solving these validated problems will help managers in improving project quality and productivity.

: Software architecture is critical to the quality of a software-intensive system. For an acquisition organization, such as the Department of Defense (DoD), the ability to evaluate software architectures early in an acquisition can have a favorable impact on the delivered system. This technical note discusses the role of software architecture evaluations in a system acquisition and describes the contractual elements that are needed to accommodate architecture evaluations in an acquisition. The note then provides an example of contractual language that incorporates the Architecture Tradeoff Analysis Method (ATAM) as a software architecture evaluation method in a system acquisition.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Logistics processes are characterized by a high degree of complexity, dynamics, diversity, and human-centricity. The reasons are manifold - product complexity, unexpected events (such as strikes, weather conditions, accidents, etc.), and the high number of participants in the logistics processes. Improving our understanding and making the process more transparent can help to avoid risks and increase performance. The main purpose of this paper is to show a practical application of process mining in the specific domain of seaport terminals. We apply process mining techniques to real port data and discover process models without a priori knowledge. A new framework for the improvement of process mining results using context-aware information in terms of seaports is proposed.

In the context of Industry 4.0, manufacturing companies have been increasingly adopting digital technologies such as Internet of Things, data analytics and cyber-physical systems to seize opportunities for productivity improvements. At the same time, established manufacturing philosophies such as Group Technology have assisted companies in managing the complexity of production processes for decades. To support manufacturing management with more informed decision-making tools, the literature has been proposing new approaches that exploit the potential of digital technologies to enhance the effectiveness of traditional manufacturing techniques. This study focuses on Production Flow Analysis (PFA) as an established approach for Group Technology. Although the existing literature has been focusing on Artificial Intelligence (AI) based approaches to plan the change to Group Technology for decades, few studies rely on production data directly extracted from the factory floor. This is partly due to the fact that technologies such as sensors and data analytics have been increasingly adopted in recent years, and this has led to an increasing amount of data that can be exploited to develop models that can support decisions. In particular, in the context of Industry 4.0, process mining has gained increasing interest, as it provides a data-driven methodology to capture real production processes. The goal of this study is to explore how PFA has evolved in the last decade thanks to the adoption of digital technologies, and to investigate potential synergies between PFA and process mining. This study uses a structured literature review to map advances in industrial applications of PFA in relation to digital technologies, as well as process mining applications in manufacturing to present a future research agenda. This provides manufacturing managers with a structured overview of existing industrial applications and the digital technologies adopted to enhance decision-making tools.