Process Mining Algorithms Research Articles

Statistical Verification of Process Model Conformance to Execution Log Considering Model Abstraction

In Big data and IoT environments, process execution generates huge-sized data some of which is subsequently obtained by sensors. The main issue in such areas has been the necessity of analyzing data in order to suggest enhancements to processes. In this regard, evaluation of process model conformance to the execution log is of great importance. For this purpose, previous reports on process mining approaches have advocated conformance checking by fitness measure, which is a process that uses token replay and node-arc relations based on Petri net. However, fitness measure so far has not considered statistical significance, but just offers a numeric ratio. We herein propose a statistical verification method based on the Kolmogorov–Smirnov (K–S) test to judge whether two different log datasets follow the same process model. Our method can be easily extended to determinations that process execution actually follows a process model, by playing out the model and generating event log data from it. Additionally, in order to solve the problem of the trade-off between model abstraction and process conformance, we also propose the new concepts of Confidence Interval of Abstraction Value (CIAV) and Maximum Confidence Abstraction Value (MCAV). We showed that our method can be applied to any process mining algorithm (e.g. heuristic mining, fuzzy mining) that has parameters related to model abstraction. We expect that our method will come to be widely utilized in many applications dealing with business process enhancement involving process-model and execution-log analyses.

Interactive mining and retrieval from process traces

The traces of past process executions are maintained in many contexts, since they constitute a strategic source of information. Different tasks on such data can be supported. In particular, we focus on process model discovery, by proposing an approach that helps the analyst in identifying a good balance between overfitting and underfitting. To achieve such a goal, we have designed SIM (Semantic Interactive Miner), an innovative interactive and incremental tool, which starts from a non-generalized model, and provides the user with a path retrieval facility to analyse the current model, and with semantic abstractions to build increasingly more generalized models (through the selective merging of retrieved paths). Additionally, the tool exploits the path retrieval facility and an indexing strategy to support efficient trace retrieval. As a consequence, our framework represents the first literature contribution able to integrate in a synergic approach process model discovery, path retrieval, and trace retrieval.We experimentally compare our tool to two well-known process mining algorithms, namely inductive miner (Leemans, Fahland, and van der Aalst, 2013) and heuristic miner (Weijters, van der Aalst, and de Medeiros, 2006). The comparison enlights the main innovative aspect of our approach, i.e., its ability to facilitate the analyst in directly using her/his domain knowledge to lead process model discovery, a feature that can be extremely advantageous in knowledge-rich applications, such as the medical ones.

Process mining-based medical program evolution

Medical path of disease varies due to the changes in external factors, and the key to the changes is the decision-making in symptomatic treatment by medical experts; how to make full use of historical medical data and recommend high quality treatment options to new cases under the current circumstance of data explosion, is the research focus of this paper. This paper first presents the standard for medical path based on cloud platform, going into the construction of workflow-based flow model of medical services; finally, a process mining algorithm based on medical evolution factor is given to realize the evolution of online medical program of a disease.

An Anti-Noise Process Mining Algorithm Based on Minimum Spanning Tree Clustering

Many human-centric systems have begun to use business process management technology in production. With the operation of business process management systems, more and more business process logs and human-centric data have been accumulated. However, the effective utilization and analysis of these event logs are challenges that people need to solve urgently. Process mining technology is a branch of business process management technology. It can extract process knowledge from event logs and build process models, which helps to detect and improve business processes. The current process mining algorithms are inadequate in dealing with log noise. The family of alpha-algorithms ignores the impact of noise, which is unrealistic in real-life logs. Most of the process mining algorithms that can handle noise also lack reasonable denoising thresholds. In this paper, a new assumption on noise is given. Furthermore, an anti-noise process mining algorithm that can deal with noise is proposed. The decision rules of the selective, parallel, and non-free choice structures are also given. The proposed algorithm framework discovers the process model and transforms it into a Petri network representation. We calculate the distance between traces to build the minimum spanning tree on which clusters are generated. The traces of the non-largest clusters are treated as noise, and the largest cluster is mined. Finally, the algorithm can discover the regular routing structure and solve the problem of noise. The experimental results show the correctness of the algorithm when compared with the $\alpha$ ++ algorithm.

Hierarchy Process Mining from Multi-Source Logs

Nowadays, large-scale business processes is growing rapidly; in this regards process mining is required to discover and enhance business processes in different departments of an organization. A process mining algorithm can generally discover the process model of an organization without considering the detailed process models of the departments, and the relationship among departments. The exchange of messages among departments can produce asynchronous activities among department process models. The event logs from departments can be considered as multi-source logs, which cause difficulties in mining the process model. Discovering process models from multi-source logs is still in the state of the art, therefore this paper proposes a hierarchy high-to-low process mining approach to discover the process model from a complex multi-source and heterogeneous event logs collected from distributed departments. The proposed method involves three steps; i.e. firstly a high level process model is developed; secondly a separate low level process model is discovered from multi-source logs; finally the Petri net refinement operation is used to integrate the discovered process models. The refinement operation replaced the abctract transitions of a high level process model with the corresponding low level process models. Multi-source event logs from several departments of a yarn manufacturing were used in the computational study, and the results showed that the proposed method combined with the modified time-based heuristics miner could discover a correct parallel process business model containing XOR, AND, and OR relations.

Constraints Resolution in Semi-Automated Course Allocation Process for Educational Institute

In this study, the problem of biasness and unfairness in course allocation mechanism is addressed. Institutions face problem when allocating courses to the faculty members that are according to their expertise and this becomes more complex when two or more than two individuals are having expertise in same area. The work done previously in this area is extended by suggesting the mechanism that reduce the human biasness and encourages the fairness to the extent possible. Mechanism suggested uses well known process mining algorithm, the Alpha. It generates process model based on software generated event logs and comparison of original process model with process mining generated process model reveals the deviations in process model which in turn helps to detect deviations in it and is founding stone for suggestive conclusion for further improvement and modification in the software process model and the software system itself. Conclusively the suggested mechanism helps to reduce human biasness in course allocation and encourages to allow fair allocation of the resources to the right person.

Constraints Resolution in Semi-Automated Course Allocation Process for Educational Institute

In this study, the problem of biasness and unfairness in course allocation mechanism is addressed. Institutions face problem when allocating courses to the faculty members that are according to their expertise and this becomes more complex when two or more than two individuals are having expertise in same area. The work done previously in this area is extended by suggesting the mechanism that reduce the human biasness and encourages the fairness to the extent possible. Mechanism suggested uses well known process mining algorithm, the Alpha. It generates process model based on software generated event logs and comparison of original process model with process mining generated process model reveals the deviations in process model which in turn helps to detect deviations in it and is founding stone for suggestive conclusion for further improvement and modification in the software process model and the software system itself. Conclusively the suggested mechanism helps to reduce human biasness in course allocation and encourages to allow fair allocation of the resources to the right person.

Financial process mining - Accounting data structure dependent control flow inference

The increasing integration of computer technology for the processing of business transactions and the growing amount of financially relevant data in organizations create new challenges for external auditors. The availability of digital data opens up new opportunities for innovative audit procedures. Process mining can be used as a novel data analysis technique to support auditors in this context. Process mining algorithms produce process models by analyzing recorded event logs. Contemporary general purpose mining algorithms commonly use the temporal order of recorded events for determining the control flow in mined process models. The presented research shows how data dependencies related to the accounting structure of recorded events can be used as an alternative to the temporal order of events for discovering the control flow. The generated models provide accurate information on the control flow from an accounting perspective and show a lower complexity compared to those generated using timestamp dependencies. The presented research follows a design science research approach and uses three different real world data sets for evaluation purposes.

ИССЛЕДОВАНИЕ ДЕСКРИПТИВНОЙ МОДЕЛИ ПРОЦЕССА ОБРАБОТКИ СУДНА НА НЕФТЯНОМ ТЕРМИНАЛЕ НА ОСНОВЕ ЗАПИСЕЙ ИЗ ТАЙМШИТОВ

Modern information systems and technologies make it possible to store a large amount of data on transport-logistics processes. Using various mathematical algorithms and meth-ods of the data mining and machine learning, knowledge can be obtained and expressed as a behavioral model of the system. The process mining method is one of the relatively new methods and it is already widely used in different areas. Due to this method, one can simulate the whole process as it is and lately work with a valid re-flection of the actual operations. Theoretically, transport-logistics processes are an example of reg-ulated and structured processes. However, in practice, normative and descriptive models often have a lot of differences and such deviations cause bottlenecks in the workflow, and thus lead to temporal and financial losses. With the help of Process Mining algorithms, the actual process of ship entry processing at the oil terminal is represented as an undirected graph. The existing problems of port systems are marked, as well as the need to work with the actual process models. The possible options to optimize processes and, consequently, improve the competitiveness of the port were considered. The schemes of transport processes, in which sea port is involved, were presented. The place of the analysed processes in the overall process scheme is defined. The methods and documents involved in the process of creating the model were identified. Two received models of vessel’s treatment were reflected and the main results of the analysis were described, problems and possible solutions were listed.

Pattern Mining and Process Modelling of Collaborative Interaction Data in an Online Multi-Tabletop Learning Environment

This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalisation and simplicity. The results showed that alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalisation. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalisation and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalisation. Moreover, the models/graphs generated through fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.

A Process Mining Based Service Composition Approach for Mobile Information Systems

Due to the growing trend in applying big data and cloud computing technologies in information systems, it is becoming an important issue to handle the connection between large scale of data and the associated business processes in the Internet of Everything (IoE) environment. Service composition as a widely used phase in system development has some limits when the complexity of relationship among data increases. Considering the expanding scale and the variety of devices in mobile information systems, a process mining based service composition approach is proposed in this paper in order to improve the adaptiveness and efficiency of compositions. Firstly, a preprocessing is conducted to extract existing service execution information from server-side logs. Then process mining algorithms are applied to discover the overall event sequence with preprocessed data. After that, a scene-based service composition is applied to aggregate scene information and relocate services of the system. Finally, a case study that applied the work in mobile medical application proves that the approach is practical and valuable in improving service composition adaptiveness and efficiency.

Pattern mining and process modelling of collaborative interaction data in an online multi-tabletop learning environment

This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalisation and simplicity. The results showed that alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalisation. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalisation and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalisation. Moreover, the models/graphs generated through fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.

A Multi-Level Process Mining Framework for Correlating and Clustering of Biomedical Activities using Event Logs

Cost, time and resources are major factors affecting the quality of hospitals business processes. Bio-medical processes are twisted, unstructured and based on time series making it difficult to do proper process modeling for them. On other hand, Process mining can be used to provide an accurate view of biomedical processes and their execution. Extracting process models from biomedical code sequenced data logs is a big challenge for process mining as it doesn’t provide business entities for workflow modeling. This paper explores application of process mining in biomedical domain through real-time case study of hepatitis patients. To generate event logs from big datasets, preprocessing techniques and LOG Generator tool is designed. To reduce complexity of generated process model, a multilevel process mining framework including text similarity clustering algorithm based on Levenshtein Distance is proposed for event logs to eliminate spaghetti processes. Social network models and four distinct types of sub workflow models are evaluated using specific process mining algorithms.

Structural Similarity Measurement of Business Process Model to Compare Heuristic and Inductive Miner Algorithms Performance in Dealing with Noise

Heuristic and Inductive Miner algorithms have different characteristics, properties, specializations, and performances in modeling business process. This research adopts three modules from Wang framework i.e. log generation, process mining, and similarity calculation to compare process mining algorithms performance in dealing with noise. The similarity calculation module measures structural similarity between reference model generated from standard event log, with mined model generated from noisy event log obtained with Heuristic and Inductive Miner algorithms. Noisy event log is obtained by adding 1% noise to the standard event log. Results from stuctural similarity measurement show that Inductive Miner algorithm obtain better performance in dealing with noise, especially related to material procurement process model of Cement Manufacturing and production in Pharmaceutical Industry.

A Co-Training Strategy for Multiple View Clustering in Process Mining

Process mining refers to the discovery, conformance, and enhancement of process models from event logs currently produced by several information systems (e.g. workflow management systems). By tightly coupling event logs and process models, process mining makes it possible to detect deviations, predict delays, support decision making, and recommend process redesigns.Event logs are data sets containing the executions (called traces) of a business process. Several process mining algorithms have been defined to mine event logs and deliver valuable models (e.g. Petri nets) of how logged processes are being executed. However, they often generate spaghetti-like process models, which can be hard to understand. This is caused by the inherent complexity of real-life processes, which tend to be less structured and more flexible than what the stakeholders typically expect. In particular, spaghetti-like process models are discovered when all possible behaviors are shown in a single model as a result of considering the set of traces in the event log all at once.To minimize this problem, trace clustering can be used as a preprocessing step. It splits up an event log into clusters of similar traces, so as to handle variability in the recorded behavior and facilitate process model discovery. In this paper, we investigate a multiple view aware approach to trace clustering, based on a co-training strategy. In an assessment, using benchmark event logs, we show that the presented algorithm is able to discover a clustering pattern of the log, such that related traces result appropriately clustered. We evaluate the significance of the formed clusters using established machine learning and process mining metrics.

Scalable Process Discovery Using Map-Reduce

Process discovery is an approach to extract process models from event logs. Given the distributed nature of modern information systems, event logs are likely to be distributed across different physical machines. Map-Reduce is a scalable approach for efficient computations on distributed data. In this paper we present Map-Reduce implementations of two well-known process mining algorithms to take advantage of the scalability of the Map-Reduce approach. We present the design of a series of mappers and reducers to compute the log-based ordering relations from distributed event logs. These can then be used to discover a process model. We provide experimental results that show the performance and scalability of our implementations.

Statistical relational learning for workflow mining

The management of business processes can support efficiency improvements in organizations. One of the most interesting problems is the mining and representation of process models in a declarative language. Various recently proposed knowledge-based languages showed advantages over graph-based procedural notations. Moreover, rapid changes of the environment require organizations to check how compliant are new process instances with the deployed models. We present a Statistical Relational Learning approach to Workflow Mining that takes into account both flexibility and uncertainty in real environments. It performs automatic discovery of process models expressed in a probabilistic logic. It uses the existing DPML algorithm for extracting first-order logic constraints from process logs. The constraints are then translated into Markov Logic to learn their weights. Inference on the resulting Markov Logic model allows a probabilistic classification of test traces, by assigning them the probability of being compliant to the model. We applied this approach to three datasets and compared it with DPML alone, five Petri netand EPC-based process mining algorithms and Tilde. The technique is able to better classify new execution traces, showing higher accuracy and areas under the PR/ROC curves in most cases.

Modified Time-Based Heuristics Miner for Parallel Business Processes

Process Mining, or Process Discovery, is a method for modeling the workflow of a business process from event logs. Business process models contain sequential and parallel traces. In this paper, a modification of the frequently used process-mining algorithm Heuristics Miner is proposed. The proposed algorithm is called Modified Time-based Heuristics Miner because it considers not only the sequence of activities but also the time-based information from the event log. It can effectively distinguish parallel (AND), single choice (XOR) and conditional (OR) patterns; the latter cannot be discovered by the original Heuristics Miner. The threshold intervals are determined on the basis of the average dependency measure in the dependency graph. The experimental results show that the proposed algorithm is able to discover concurrent business processes formed by parallel (AND) and conditional (OR) patterns, whereas the existing Heuristics Miner algorithm can only discover concurrent business processes formed by parallel (AND) patterns. This paper also provides an evaluation of validity an fitness of the discovered process model.

Healthcare Analysis using Mining Algorithm on Mobile Technology

The healthcare analysis for the patient through the consulting service using mobile technology is proposed in this paper. A new MMM (Min Mapping Miner) process mining algorithm is introduced to overcome the limitation of existing pitfalls in H Mine algorithm. This algorithm has a unique feature. It uses limited memory and can work in parallel process. The analysis of healthcare handled with MMM, is actually a frequent pattern mining algorithm. The MMM frequent pattern mining algorithm has better confidence and performance for different treatment process for patients, where the doctors can envisage combination of treatments in the consulting service. Till now, the patient treatment has been represented using text or graphical representation, where as the MMM algorithm can represent using association rules. The confidences of association rules are exact to comprehend the patient treatment diagnosing process. The major focus of this article is to deal with the flow of treatment process in mobile device. A mobile technology is used to give the prompt understanding of the treatment given by the doctors to the patients, which are recorded in the form of event logs and stored as historical data. Hence, this development primarily contributes to knowledge discovery of various healthcare treatment diagnoses for the patient consulting services.

A process-mining-based scenarios generation method for SOA application development

Business process models which are usually constructed by business designers from experience and analysis are the main guidelines for services composition in the service-oriented architecture (SOA) applications development. However, due to the complexity of business models, it is a challenging task for business process designers to optimize the process models dynamically in accordance with changes in business environments. In this paper, a process-mining-based method is proposed to support business process designers to monitor efficiency or capture the changes of a business process. Firstly, we define a scenario model to depict business elements and their relationships which are critical to business process design. Based on the proposed scenario model, process mining algorithms, including control flow mining, roles mining and data flow mining are carried out in a certain sequence synthetically to extract business scenarios from event logs recorded by SOA application systems. Finally, we implement a prototype using a logistic scenario to illustrate the feasibility of our method in SOA applications development.