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In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today's process aware information systems such as 'Enterprise Resource Planning' systems, 'Business Process Management' systems, 'Supply Chain Management' systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks. [ABSTRACT FROM AUTHOR]

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a "three-dimensional" view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. [ABSTRACT FROM AUTHOR]

The need for the analysis of modern businesses is rapidly increasing as the supporting enterprise systems generate more and more data. This data can be extremely valuable for executing organizations because the data allows constant monitoring, analyzing, and improving the underlying processes, which leads to the reduction of cost and the improvement of the quality. Process mining is a useful technique for analyzing enterprise systems by using an event log that contains behaviours. This research focuses on the process discovery and refinement using real-life event log data collected from a large multinational organization that deals with coatings and paints. By investigating and analyzing their order handling processes, this study aims at learning a model that gives insight inspection of the processes and performance analysis. Furthermore, the animation is also performed for the better inspection, diagnostics, and compliance-related questions to specify the system. The configuration of the system and the conformance checking for further enhancement is also addressed in this research. To achieve the objectives, this research uses process mining techniques, i.e. process discovery in the form of formal Petri nets models with the help of process maps, and process refinement through conformance checking and enhancement. Initially, the identified executed process is reconstructed by using the process discovery techniques. Following the reconstruction, we perform a deep analysis for the underlying process to ensure the process improvement and redesigning. Finally, some recommendations are made to improve the enterprise management system processes. [ABSTRACT FROM AUTHOR]

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a “three-dimensional” view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie.

Data centres have become the backbone of large Cloud services and applications, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining techniques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces. [ABSTRACT FROM AUTHOR]

Nowadays, Business processes in organizations are supported by information systems. These systems record organizational processes outputs in the form of event logs, which contain valuable information about processes and their performance. Process mining extract knowledge from event logs. One of the most important tasks in process mining is process model discovery that uses an algorithm to build a process model from a given event log. In this research, a new model which named ICMA proposed for discovering process models. This model has three steps, pre-processing phase, body of model and post-processing phase. Imperialist Competitive Algorithm (ICA) was used for the first time as body of proposed model. Nine hundred nineteen event logs were used, those are balanced event logs, unbalanced event logs, and real-life event logs. Moreover, those event logs were studied at the 0%, 1%, 5%, 10% and 20% noise levels and the results have compared to the recent Vazquise algorithm. The research findings revealed that precision and completeness of our model is better than Vazquez model. In this paper has been shown that the ICMA model compared to the other approaches in literature method drastically improved the precision and completeness of the model. In addition, the noise problem was satisfactorily solved through data pre-processing and post-processing operations. [ABSTRACT FROM AUTHOR]

Process discovery algorithms aim to capture process models from event logs. These algorithms have been designed for logs in which the events that belong to the same case are related to each other - and to that case - by means of a unique case identifier. However, in service-oriented systems, these case identifiers are rarely stored beyond request-response pairs, which makes it hard to relate events that belong to the same case. This is known as the correlation challenge. This paper addresses the correlation challenge by introducing a technique, called the correlation miner, that facilitates discovery of business process models when events are not associated with a case identifier. It extends previous work on the correlation miner, by not only enabling the discovery of the process model, but also detecting which events belong to the same case. Experiments performed on both synthetic and real-world event logs show the applicability of the correlation miner. The resulting technique enables us to observe a service-oriented system and determine - with high accuracy - which request-response pairs sent by different communicating parties are related to each other. [ABSTRACT FROM AUTHOR]

Traditional process mining techniques offer limited possibilities to analyze business processes working in low-predictable and dynamic environments. Recently, to close this gap, declarative process models have been introduced to represent process mining results since they allow for describing complex behaviors as a compact set of business rules. However, in this context, activities of a business process are still considered as atomic/instantaneous events. This is a strong limitation for these approaches because often, in realistic environments, process activities are not instantaneous but executed across a time interval and pass through a sequence of states of a lifecycle. This paper investigates how the existing techniques for the discovery of declarative process models can be adapted when the business process under analysis contains non-atomic activities. In particular, we base our proposed approach on the use of discriminative rule mining to determine how the characteristics of the activity lifecycles in a business process influence the validity of a business rule in that process. The approach has been implemented as a plug-in of the process mining tool ProM and validated on synthetic logs and on a real-life log recorded by an incident and problem management system called VINST in use at Volvo IT Belgium. [ABSTRACT FROM AUTHOR]