Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining

Process mining (PM) is a technique to extract a process model from an event log to represent the process behaviour recorded in that event log. A mined process model with high fitness means that it can reflect most of the process behaviour recorded in the event log. Previous studies have shown that the mined model with high fitness can be used in process improvement, such as fraud detection, continuous process improvement and benchmarking. Genetic process mining (GPM) is a famous PM approach, which can simultaneously identify several process structures from event logs. However, GPM cannot effectively discover parallel structures from event logs. This study proposes a PM approach based on integration of GPM, particle swarm optimisation and differential evolution to find process models with high fitness for event logs involving multiple parallel structures. The results show that the proposed approach does indeed lead to improvement in gaining process models with high fitness for event logs involving multiple parallel structures.

Process mining, especially process discovery, has been utilized to extract process models from event logs. One challenge faced by process discovery is to identify concurrency effectively. State-of-the-art approaches employ activity orders in traces to undertake process discovery and they require stringent completeness notions of event logs. Thus, they may fail to extract appropriate processes when event logs cannot meet the completeness criteria. To address this problem, we propose in this paper a novel technique which leverages activity dependences in traces. Based on the observation that activities with no dependencies can be executed in parallel, our technique is in a position to discover processes with concurrencies even if the logs fail to meet the completeness criteria. That is, our technique calls for a weaker notion of completeness. We evaluate our technique through experiments on both real-world and synthetic event logs, and the conformance checking results demonstrate the effectiveness of our technique and its relative advantages compared with state-of-the-art approaches.

Process mining techniques provide an opportunity for discovering information about process models in organizations or web based systems based on the execution of data registered in event logs. Lack of logical inference and observable abstractions on the data of the generated process models limit our analysis to nothing more than the bare bone structure regarding processes and connections among them. Ontology is the most intuitive yet powerful solution that can connect traditional data mining techniques and process mining techniques enriching the analysis based on specific or in general queries on the event logs or databases. Our objective of this paper is to translate the entirety of the process mining techniques to ontological representations. Performance matrices and process discovery techniques are the main aspects that need to be converted to ontological concepts. For analysis on real world event logs, we have chosen stack-overflow data in order to provide a tag based prediction technique.

The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a genetic setting. In this paper, we show a genetic process mining approach using the so-called causal matrix as a representation for individuals. We elaborate on the relation between Petri nets and this representation and show that genetic algorithms can be used to discover Petri net models from event logs.KeywordsProcess MiningPetri NetsGenetic AlgorithmsProcess DiscoveryBusiness Process IntelligenceBusiness Activity Monitoring

Process mining aims at discovering process models from data logs in order to offer insight into the real use of information systems. Most of the existing process mining algorithms fail to discover complex constructs or have problems dealing with noise and infrequent behavior. The genetic process mining algorithm overcomes these issues by using genetic operators to search for the fittest solution in the space of all possible process models. The main disadvantage of genetic process mining is the required computation time. In this paper we present a coarse-grained distributed variant of the genetic miner that reduces the computation time. The degree of the improvement obtained highly depends on the parameter values and event logs characteristics. We perform an empirical evaluation to determine guidelines for setting the parameters of the distributed algorithm.

Business processes change over time in response to changing circumstances, it is crucial for process managers to discover and understand such changes from event logs. While existing techniques in process mining cannot efficiently detect and locate concept drifts in uncompleted event logs. Hence, an appropriate method, which could prompt to unravel the process evolution, may greatly assist organizations to manage the flexibility and change of business processes in the context of business process management (BMP). In this paper, we first proposed an extensible feature and employed it with the sliding window technique and heuristic miner for detecting and locating concept drifts in uncompleted event logs. Then, Genetic Process Mining (GPM) was improved by the Weight Heuristic Miner (WHM) and Differential Evolution (DE) for mining the new process model of an evolving process. Finally, experimental results on four event logs which are created based on a real-world business process have validated the proposed method.

Information systems have been widely used to support workflow processes to record the execution of tasks in the process and are stored in so-called “event logs”. Techniques that relate to events extraction have gotten increasing attention such as process mining techniques. Developed process mining methods such as alpha algorithm, alpha++ algorithm, and genetic process mining (GPM) are capable of tackling several structures well, but they are still difficult to discover parallelism structures efficiently since the parallelism structures are too complex. This work presents an evolutionary-based process mining approach based on a hybrid of GPM and particle swarm optimization algorithm (PSO) in order to handle parallelism structures. The medical records of acute stroke patients of Taiwanese medical institution are used as a practical case to test the proposed approach. Experimental results on the case show the effectiveness of the proposed approach for tackling parallelism structures.

Process mining techniques have matured over the last decade and more and more organization started to use this new technology. The two most important types of process mining are process discovery (i.e., learning a process model from example behavior recorded in an event log) and conformance checking (i.e., comparing modeled behavior with observed behavior). Process mining is motivated by the availability of event data. However, as event logs become larger (say terabytes), performance becomes a concern. The only way to handle larger applications while ensuring acceptable response times, is to distribute analysis over a network of computers (e.g., multicore systems, grids, and clouds). This paper provides an overview of the different ways in which process mining problems can be distributed. We identify three types of distribution: replication, a horizontal partitioning of the event log, and a vertical partitioning of the event log. These types are discussed in the context of both procedural (e.g., Petri nets) and declarative process models. Most challenging is the horizontal partitioning of event logs in the context of procedural models. Therefore, a new approach to decompose Petri nets and associated event logs is presented. This approach illustrates that process mining problems can be distributed in various ways.

Process mining is based on event logs. Traditionally, an event log is a sequence of events. Yet, there is a growing amount of work in the literature that suggests we should extend the notion of an event log and use partially ordered logs as a basis for process mining. Thus, the need for algorithms able to handle these partially ordered logs will grow in the upcoming years. In this paper, we adapt an existing, classical process discovery algorithm to be able to handle partially ordered logs. We use the ILP Miner [1] as a basis and replace its region theory part by compact tokenflow regions [2] to introduce the ILP2 Miner. This ILP2 Miner handles sequential event logs just like the ILP Miner but, in addition, is able to directly process partially ordered logs. We prove that the ILP2 Miner provides the same guarantees regarding structural and behavioral properties of the discovered process models as the ILP Miner. We implement the ILP2 Miner and show experimental results of its runtime using three well-known example log files from the process mining community literature.

Process mining has piqued the interest of researchers and technology manufacturers. Process mining aims to extract information from event activities and their interdependencies from events recorded by some enterprise systems. An enterprise system’s transactions are labeled based on their information content, such as an activity that causes the occurrence of another, the timestamp between events, and the resource from which the transaction originated. This paper describes a novel process of parsing event-log activities based on information content (IC). The information content of attributes, especially activity names, which are used to describe the flow processes of enterprise systems, is grouped hierarchically as hypernyms and hyponyms in a subsume tree. The least common subsume (LCS) values of these activity names are calculated, and the corresponding relatedness values between them are obtained. These values are used to create a fuzzy causal matrix (FCM) for parsing the activities, from which a process mining algorithm is designed to mine the structural and semantic relationships among activities using an enhanced gray wolf optimizer and backpropagation algorithm. The proposed approach is resistant to noisy and incomplete event logs and can be used for process mining to reflect the structure and behavior of event logs.

Process mining explores event logs to offer valuable insights to business process managers. Some types of business processes are hard to mine, including unstructured and knowledge-intensive processes. Then, trace clustering is usually applied to event logs aiming to break it into sublogs, making it more amenable to the typical process mining task. However, applying clustering algorithms involves decisions, such as how traces are represented, that can lead to better results. In this paper, we compare four vector space models for trace clustering, using them with an agglomerative clustering algorithm in synthetic and real-world event logs. Our analyses suggest the embeddings-based vector space model can properly handle trace clustering in unstructured processes.

Process mining is an emerging discipline incorporating methods and tools for the analysis of system/process executions captured in the form of event logs [1]. Traditionally process mining can be divided into three research areas: discovery (construction of process models from event logs), conformance checking (finding log and model deviations), and enhancement of existing process models with additional event log data.BPMN (Business Process Model and Notation) 2.0 [2] is a widely used process modeling notation, supported by various process modeling and analysis tools, and is a de-facto process modeling standard. Using BPMN within process mining opens perspectives for applicability of the existing process mining techniques: for instance discovered process models can be analyzed or enacted using existing BPMN-based software, and vice versa, manually created models can be imported to a process mining tool, verified against event logs, and enhanced with additional data.In this work we bridge the gap between conventional process modeling formalisms used in the context of process mining (e.g., Petri nets, causal nets, process trees) and BPMN. For that purpose we developed a suite of conversion algorithms and provide formal guarantees relating the behavior of Petri nets (including non-free-choice nets) to the corresponding BPMN models (and vice versa). The derived relations are used to enhance the BPMN models with information learned from the event logs.The developed conversion techniques are described in detail in [3] and have been implemented [4] as a part of ProM (Process Mining Framework) [5] -- an open source tool for process mining and verified on real event log data. Moreover, cases for which conversion algorithms give more compact process models in comparison with the initial models are identified. Although the developed algorithms deal with basic control flow constructs only, they can be applied in the discovery of advanced BPMN modeling elements [2], including subprocesses [6-7], cancellations [8], conditional branching and data objects, swimlanes, message flows, and others.

Process mining combines data mining with process analysis, e.g. to discover process models from event logs. Practice shows that event logs grow very fast. Consequently, they quickly become too large to analyze with current tools. Given the exploratory nature of many process mining algorithms, this can be problematic, as in many cases algorithms are used frequently to optimize and analyze the influence of parameters. One solution is reducing the data by sampling the event log. Many sampling approaches exist, yet the quality of these approaches is unknown. In this paper, we study the behavioral quality of event log sampling, and introduce measures to quantify this behavioral quality. The approach has been implemented in the tool ProM. Experiments show that sampling very quickly introduces under and oversampled behavior in the event log, which can be problematic for frequency-based algorithms.

Event logs are the primary source of information in process mining. An event log consists of events, each having three mandatory attributes: a case identifier, an activity, and a timestamp. Translucent event logs add a fourth mandatory attribute: enabled activities. These are activities for which execution was possible besides the executed activity. Information on enabled activities can be accessed in task management software, in tasks executed within a desktop environment, or by utilizing domain knowledge. Information on enabled activities is valuable for process discovery and other process mining tasks. For example, utilizing the information when discovering process models from translucent event logs results in more robust and generalizable process models. In recent work, translucent activity relationships were defined, and their application was shown by extending the inductive mining approach. This work extends the defined relationships by also introducing frequencies. As a result, the Inductive Miner—infrequent can be extended. Hence, it is possible to use these relationships in the presence of noise in the recorded event logs. We introduce three translucent variants for the Inductive Miner—infrequent, each using translucent activity relationships at different points. Moreover, we allow for different graphs when fall-throughs within the Inductive Miner occur. Using artificial and enriched real-life event logs, we show that considering these relationships, more data-specific process models that still have desired generalization capabilities can be discovered.

In recent years, process mining has shifted towards an object-centric perspective on processes, considering interacting sub-processes that operate on objects from different types. Since businesses are hesitant to publicly share such event data that may expose business internals, there is a lack of public real-world object-centric event logs. We propose a novel approach to use publicly accessible game data as a large-scale data source for object-centric event logs. The contributions of this paper include (1) a framework to extract object-centric event logs from real-time strategy game data, (2) an application of that framework for the game Age of Empires II, (3) a published Python library to automatically transform Age of Empire gameplays into an object-centric event log, (4) a publicly accessible object-centric event log extracted from 325,398 gameplays, and (5) an evaluation of the published aoe2pm library. The evaluation shows that the extracted event logs can be used by state-of-the-art applications to generate relevant behavior insights that require an object-centric perspective. The size and attribute richness of the object-centric event log motivate future research questions in the field of object-centric process mining.