A survey of graph-based algorithms for discovering business processes

Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algorithm, i.e. Alpha Miner and its expansion. Other than that, this research also gives outline explanations about graph-based algorithms and their focus issues. Based on the evaluations, the graph-based algorithm has high performance and less time complexity than Alpha Miner algorithm

Artifact Lifecycle Discovery

Artifact-centric modeling is a promising approach for modeling business processes based on the so-called business artifacts - key entities driving the company's operations and whose lifecycles define the overall business process. While artifact-centric modeling shows significant advantages, the overwhelming majority of existing process mining methods cannot be applied (directly) as they are tailored to discover monolithic process models. This paper addresses the problem by proposing a chain of methods that can be applied to discover artifact lifecycle models in Guard-Stage-Milestone notation. We decompose the problem in such a way that a wide range of existing (non-artifact-centric) process discovery and analysis methods can be reused in a flexible manner. The methods presented in this paper are implemented as software plug-ins for ProM, a generic open-source framework and architecture for implementing process mining tools

A Role of Heuristics Miner Algorithm in the Business Process System

The main idea of process mining is to extract knowledge or information from event logs recorded by an information system. Till now, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Process mining has become a bright research area. In this paper we discuss the challenging process mining domain and demonstrate a heuristics driven process mining algorithm; the so called Heuristics Miner in detail. Heuristics Miner is a practical applicable mining algorithm that can deal with noise, and can be used to express the main behavior that is not all details and exceptions, registered in an event log. The business process system has a complex process system, which deals about many cases or audit trail entries and various event logs. This paper deals about the role of Heuristics Miner algorithm in the field of Business process system, also the Heuristics Miner algorithm is compared with the other mining algorithm such as α-algorithm. Hence, we analyzed that, is it possible to develop a control flow process mining algorithm such as Heuristics Miner algorithm can discover all the common control flow structures and is robust to noisy logs at once? This paper attempts to provide an answer to this question

Anomaly Detection based on Control-flow Pattern of Parallel Business Processes

The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities

Repairing Process Models with Non-Free-Choice Constructs Based on Token Replay

A method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers

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