Applying Process Mining Algorithms in the Context of Data Collection Scenarios

Despite the technological progress, paper-based questionnaires are still widely used to collect data in many application domains like education, healthcare or psychology. To facilitate the enormous amount of work involved in collecting, evaluating and analyzing this data, a system enabling process-driven data collection was developed. Based on generic tools, a process-driven approach for creating, processing and analyzing questionnaires was realized, in which a questionnaire is defined in terms of a process model. Due to this characteristic, process mining algorithms may be applied to event logs created during the execution of questionnaires. Moreover, new data that might not have been used in the context of questionnaires before may be collected and analyzed to provide new insights in regard to both the participant and the questionnaire. This thesis shows that process mining algorithms may be applied successfully to process-oriented questionnaires. Algorithms from the three process mining forms of process discovery, conformance checking and enhancement are applied and used for various analysis. The analysis of certain properties of discovered process models leads to new ways of generating information from questionnaires. Different techniques for conformance checking and their applicability in the context of questionnaires are evaluated. Furthermore, new data that cannot be collected from paper-based questionnaires is used to enhance questionnaires to reveal new and meaningful relationships

Process mining using BPMN : relating event logs and process models

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more involved BPMN models in comparison with the initial models are identified. Keywords: Process mining; Process discovery; Conformance checking; BPMN (Business Process Model and Notation); Petri nets; Bisimulatio

Automated repair of process models with non-local constraints using state-based region theory

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.This work was partly supported by the Australian Research Council Discovery Project DP180102839. This work was supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.Peer ReviewedPostprint (author's final draft

A Novel Approach for Process Mining : Intentional Process Models Discovery

International audienceSo 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

Mining complete, precise and simple process models

Process discovery algorithms are generally used to discover the underlying process that has been followed to achieve an objective. In general, these algorithms do not take into account any domain knowledge to derive process models, allowing to apply them in a general manner. However, depending on the selected approach, a different kind of process models can be discovered, as each technique has its strengths and weaknesses, e.g., the expressiveness of the used notation. Hence, it is important to take into account the requirements of the domain when deciding which algorithm to use, as the correct assumptions can lead to richer process models. For instance, among the different domains of application of process mining we can identify several fields that share an interesting requirement about the discovered process models. In security audits, discovered processes have to fulfill strict requisites. This means that the process model should reproduce as much behavior as possible; otherwise some violations may go undetected (replay fitness). On the other hand, in order to avoid false positives, process models should reproduce only the recorded behavior (precision). Finally, process models should be easily readable to better detect deviations (simplicity). Another clear example concerns the educational domain, as in order to be of value for both teachers and learners, a discovered learning process should satisfy the aforementioned requirements. That is, to guarantee feasible and correct evaluations, teachers need to access to all the activities performed by learners, thereby the learning process should be able to reproduce as much behavior as possible (replay fitness). Furthermore, the learning process should focus on the recorded behavior seen in the event log (precision), i.e., show only what the students did, and not what they might have done, while being easily interpretable by the teachers (simplicity). One of the previous requirements is related to the readability of process models: simplicity. In process mining, one of the identified challenges is the appropriate visualization of process models, i.e., to present the results of process discovery in such a way that people actually gain insights about the process. Process models that are unnecessary complex can hinder the real behavior of the process rather than to provide an intuition of what is really happening in an organization. However, achieving a good level of readability is not always straightforward, for instance, due the used representation. Within the different approaches focused to reduce the complexity of a process model, the interest in this PhD Thesis relies on two techniques. On the one hand, to improve the readability of an already discovered process model through the inclusion of duplicate labels. On the other hand, the hierarchization of a process model, i.e., to provide a well known structure to the process model. However, regarding the latter, this technique requires to take into account domain knowledge, as different domains may rely on different requirements when improving the readability of the process model. In other words, in order to improve the interpretability and understandability of a process model, the hierarchization has to be driven by the domain. To sum up, concerning the aim of this PhD Thesis, we can identify two main topics of interest. On the one hand, we are interested in retrieving process models that reproduce as much behavior recorded in the log as possible, without introducing unseen behavior. On the other hand, we try to reduce the complexity of the mined models in order to improve their readability. Hence, the aim of this PhD Thesis is to discover process models considering replay fitness, precision and simplicity, while paying special attention in retrieving highly interpretable process models

Dealing with Complex Parallel Structures in Process Discovery

Üks protsessikaeve eesmärkidest on leida protsessimudeleid logifailidest. Samas sõltub leitava protsessimudeli kvaliteet sellest, kui täielik informatsioon protsessi käitumise kohta logifailis on, kuna paralleelarvutuste keerukuse kasv on faktoraalses sõltuvuses harude hulgast. Selles lõputöös tutvustatakse uut algoritmi, mis kombineerib jaga-ja-valitse võtet olemasolevate kaevealgoritmidega, et täiustada hästistruktureeritud ja samaaegselt toimuvate tegumitega protsessimudelite kaevet poolikutest logifailidest. See töö kirjeldab väljapakutud algoritmi ja selgitab, kuidas see töötab samm-sammu haaval illustratiivsete kaeveprotsessi näidete abil. Lõpuks hindame selle meetodi efektiivsust ja tulemuslikkust kasutades protsessimudeleid, mis sisaldavad samaaegselt toimuvaid tegumeid ja juhuslikult loodud mudeleid.One of the aims of process mining is to discover a process model from a log. However, the quality of the discovered model depends on the completeness of the information about the process behaviour contained in the log. Incomplete logs do not provide all the possible behaviours. Existing process discovery algorithms dealing with incomplete logs, have troubles when working with complex parallel structures, because parallel behaviour has factorial rate of growth with respect to the number of branches. In this work, a new algorithm is proposed, which combines divide and conquer approach, with the existing mining algorithms to improve discovery of highly structured and highly concurrent process models from incomplete logs. This work describes the proposed algorithm, and explains how it works with illustrative step-by-step examples of the mining procedure. Finally, we evaluate the effectiveness and efficiency of our approach by using process models containing complex parallel structures and randomly generated models

Efficient Process Model Discovery Using Maximal Pattern Mining

In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques

PROCESS MODELS DISCOVERY AND TRACES CLASSIFICATION: A FUZZY-BPMN MINING APPROACH.

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any domain process, and describes causal dependencies between the various activities as performed within the process execution environment. In principle, one can use process discovery to obtain process models that describes reality. To this end, the work in this artcle presents a Fuzzy-BPMN mining approach that uses training events log representing 10 different real-time business process executions to provide a method for discovery of useful process models, and then cross-validating the derived models with a set of test event logs in order to measure the accuracy and performance of the employed approach. The method focuses on carrying out a classification task to determine the traces, i.e. individual cases that makes up the test event logs in order to determine which traces that can be replayed by the original model. Thus, the paper aim is to provide a technique for process models discovery which is as good in balancing between “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed (i.e allowed) or non-replayable (disallowed) by the model. In other words, the study shows through the Fuzzy-BPMN replaying notation and the series of validation experiments - how given any classified trace (for the test events log) and discovered process model (the training log) it can be unambiguously determined whether or not the traces found can be replayed on the discovered model