Guided Process Discovery -A Pattern-based Approach (original) (raw)
Related papers
A novel approach to process mining: Intentional process models discovery
2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS), 2014
So far, process mining techniques have suggested to model processes in terms of tasks that occur during the enactment of a process. However, research on method engineering and guidance has illustrated that many issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. This paper presents a novel approach of process mining, called Map Miner Method (MMM). This method is designed to automate the construction of intentional process models from process logs. MMM uses Hidden Markov Models to model the relationship between users' activities logs and the strategies to fulfill their intentions. The method also includes two specific algorithms developed to infer users' intentions and construct intentional process model (Map) respectively. MMM can construct Map process models with different levels of abstraction (fine-grained and coarse-grained process models) with respect to the Map metamodel formalism (i.e., metamodel that specifies intentions and strategies of process actors). This paper presents all steps toward the construction of Map process models topology. The entire method is applied on a large-scale case study (Eclipse UDC) to mine the associated intentional process. The likelihood of the obtained process model shows a satisfying efficiency for the proposed method.
Abstractions in Process Mining: A Taxonomy of Patterns
Lecture Notes in Computer Science, 2009
Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to constructing process models from raw traces without due pre-processing. In an event log, there can be instances where the system is subjected to similar execution patterns/behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of process models and can assist in defining the conceptual relationship between the tasks/activities. In this paper, we characterize and explore the manifestation of commonly used process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-processing step for most of today's process mining techniques. The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis/anomaly detection besides being an enabler for hierarchical process discovery.
Domain-driven actionable process model discovery
Computers & Industrial Engineering, 2016
Process discovery is a type of process mining that constructs a process model from the event logs of an information system. The model discovered using process discovery techniques and the process as perceived by users will always differ in some ways and to some extents. In particular, less structured process, such as operational process in business and manufacturing, often result overly confusing, spaghetti-like, process models caused by the inherent complexity of the process. As a result, the mined model has many limitations for providing the users with explicit knowledge that can be directly used to influence behavior for the user's interest. Explicit knowledge, as later called by actionable knowledge, is an important representation on measuring the interestingness of mined patterns. This actionable knowledge, which is incorporated with users' background knowledge and based on some notions of actionable rules, can result an actionable process model. Undoubtedly, domain experts, who know the process well, play a key role to enhance the mined model into an actionable model by their involvements during the discovery process. This paper presents a discovery method to obtain an actionable process model that is based on both the event relation in the log and users' knowledge to improve the incompatibility of the traditional process mining approaches. Users can set their knowledge in terms of constraints. Unlike the existing approach, the proposed approach synthesizes the activity proximity and attempts to extract behavior satisfied by the constraints which may be hidden in the event logs for resulting an actionable process model. In addition, the proposed method is used in order to achieve a sound process model when the existence of the constraints does not satisfy the workflow soundness property. The method was implemented in the ProM framework and tested on a real process.
Process discovery in event logs: An application in the telecom industry
Applied Soft Computing, 2011
The abundant availability of data is typical for information-intensive organizations. Usually, discerning knowledge from vast amounts of data is a challenge. Similarly, discovering business process models from information system event logs is definitely non-trivial. Within the analysis of event logs, process discovery, which can be defined as the automated construction of structured process models from such event logs, is an important learning task. However, the discovery of these processes poses many challenges. First of all, human-centric processes are likely to contain a lot of noise as people deviate from standard procedures. Other challenges are the discovery of so-called non-local, non-free choice constructs, duplicate activities, incomplete event logs and the inclusion of prior knowledge. In this paper, we present an empirical evaluation of three state-of-the-art process discovery techniques: Genetic Miner, AGNEs and HeuristicsMiner. Although the detailed empirical evaluation is the main contribution of this paper to the literature, an in-depth discussion of a number of different evaluation metrics for process discovery techniques and a thorough discussion of the validity issue are key contributions as well.
Process Mining: A Recent Framework for Extracting a Model from Event Logs
Atas da 17ª Conferência da Associação Portuguesa de Sistemas de Informação, 2017
Business Process Management (BPM) is a well-known discipline, with roots in previous theories related with optimizing management and improving businesses results. One can trace BPM back to the beginning of this century, although it was in more recent years when it gained a special focus of attention. Usually, traditional BPM approaches start from top and analyse the organization according some known rules from its structure or from the type of business. Process Mining (PM) is a completely different approach, since it aims to extract knowledge from event logs, which are widely present in many of today's organizations. PM uses specialized data-mining algorithms, trying to uncover patterns and trends in these logs, and it is an alternative approach where formal process specification is not easily obtainable or is not cost-effective. This paper makes a literature review of major works issued about this theme.
Automated Discovery of Process Models from Event Logs: Review and Benchmark
IEEE Transactions on Knowledge and Data Engineering, 2019
Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
Process Discovery from Event Data: Relating Models and Logs Through Abstractions
Event data are collected in logistics, manufacturing, finance, healthcare, customer relationship management, e-learning, e-government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, etc. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control-flow model from the event data. This is very challenging, but in recent years many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad-hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an "interface" between both. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block-structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also used to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps to broaden the view and facilitates the creation of new discovery approaches.
From Zero to Hero: A Process Mining Tutorial
Lecture Notes in Computer Science, 2017
Process mining is an emerging area that synergically combines model-based and data-oriented analysis techniques to obtain useful insights on how business processes are executed within an organization. This tutorial aims at providing an introduction to the key analysis techniques in process mining that allow decision makers to discover process models from data, compare expected and actual behaviors, and enrich models with key information about the actual process executions. In addition, the tutorial will present concrete tools and will provide practical skills for applying process mining in a variety of application domains, including the one of software development.
Application and Theory of Petri Nets and Concurrency, 2015
Process mining is a family of methods to analyze event logs produced during the execution of business processes in order to extract insights regarding their performance and conformance with respect to normative or expected behavior. The landscape of process mining methods and use cases has expanded considerably in the past decade. However, the field has evolved in a rather ad hoc manner without a unifying foundational theory that would allow algorithms and theoretical results developed for one process mining problem to be reused when addressing other related problems. In this paper we advocate a foundational approach to process mining based on a well-known model of concurrency, namely event structures. We outline how event structures can serve as a unified representation of behavior captured in process models and behavior captured in event logs. We then sketch how process mining operations, specifically automated process discovery, conformance checking and deviance mining, can be recast as operations on event structures.