Business Process Logs Research Articles

Data- & compute-efficient deviance mining via active learning and fast ensembles

Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks or faults). However, training a Deviance Prediction Model (DPM) by solely using supervised learning methods is impractical in scenarios where only few examples are labelled. To address this challenge, we propose an Active-Learning-based approach that leverages multiple DPMs and a temporal ensembling method that can train and merge them in a few training epochs. Our method needs expert supervision only for a few unlabelled traces exhibiting high prediction uncertainty. Tests on real data (of either complete or ongoing process instances) confirm the effectiveness of the proposed approach.

Enabling efficient business process mining using flatten sequential structure model

The volume of extensible markup language (XML) format documents is increasing every day due to the development of internet and the use of XML format in business process log file. Storing business process log data in XML format is preferable due to the ability of extensible and storing data irrespective of how it will be represented. However, mining XML format data poses challenges due to its complex data structure and dimensions. This paper proposes a method to convert XML format document into a structured format without ignoring the structural information. Converting semi-structured business process log data into structured format will allow more data mining techniques and statistical test be conducted and extract information from the business process log data. The experiment in this study performs t-test on a set of synthetic data and a set of real-world data to prove that information in business process log can be extracted through normal statistical test. Empirical results show that statistical analysis can be conducted on business process log data especially in XML format after flatten sequential structure model (FSSM) is used.

Business process recommendation method based on cost constraints

ABSTRACT Business process recommendation can be used to simplify the working procedures of enterprises, avoid unnecessary expenses, and promote the development of enterprises. In the process of process recommendation, there are a lot of activities that are similar in structure and difficult to choose. Here, a process recommendation method based on cost constraints is proposed to solve the problem of difficult to distinguish similar processes. First, the business process is transformed into a labelled Petri net, and the execution probability of each transition is calculated according to the business process log. Then, the matrix used to represent Petri nets is constructed according to the adjacent relationship between transitions, and the matrix is made into the same dimension, and the similarity between matrices is calculated by biggest–smallest approach degree, and the set of Petri nets with similar structure is established. Finally, a cost constraint-based process recommendation method is proposed to find lower service cost items in similar process sets. In the experimental part, the feasibility of the method is compared and verified.

A Framework to Analyze Business Process Log in XML Format

XML has numerous uses in a wide variety of web pages and applications. Some common uses of XML include tasks for web publishing, web searching and automation, and general application such as for utilize, store, transfer and display business process log data. The amount of information expressed in XML has gone up rapidly. Many works have been done on sensible approaches to address issues related to the handling and review of XML documents. Mining XML documents offera way to understand both the structure and the content of XML documents. A common approach capable of analysing XML documents is frequent subtree mining.Frequent subtree mining is one of the data mining techniques that finds the relationship between transactions in a tree structured database. Due to the structure and the content of XML format, traditional data mining and statistical analysis hardly applied to get accurate result. This paper proposes a framework that can flatten a tree structured data into a flat and structured data, while preserving their structure and content.Enabling these XML documents into relational structured data allows a range of data mining techniques and statistical test can be applied and conducted to extract more information from the business process log.

Concept drift detection in business process logs using deep learning

Concept drift detection in business process logs using deep learning

POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

Detecting anomalies in hybrid business process logs

Checking and analyzing various executions of different Business Processes can be a tedious task as the logs from these executions may contain lots of events, each with a (possibly large) number of both numerical and categorical attributes. We developed a way to automatically model the behavior captured in log files with dozens of these attributes. The advantage of our method is that we do not need any prior knowledge about the data or the attributes. We introduce a new algorithm that is able to learn a model of a log file starting from the data itself. The learned model can then be used to detect anomalous executions in the data. To achieve this we extend Dynamic Bayesian Networks with numerical attributes and functional dependencies to better model the normal behavior found in log files. The model is capable of scoring events and cases, even when previously unseen values or new combinations of values appear in the log file. An important benefit of our model is the ability to give a decomposition of the score that indicates the root cause of the anomalies. We also conducted a comparison with other state-of-the-art algorithms for detecting anomalies in Business Processes which shows that our approach outperforms other algorithms.

Dynamic Access Control to Semantics-Aware Streamed Process Logs

Business process logs are composed of event records generated, collected and analyzed at different locations, asynchronously and under the responsibility of different authorities. Their analysis is often delegated to auditors who have a mandate for monitoring processes and computing metrics but do not always have the rights to access the individual events used to compute them. A major challenge of this scenario is reconciling the requirements of privacy and access control with the need to continuously monitor and assess the business process. In this paper, we present a model, a language and a software toolkit for controlling access to process data where logs are made available as streams of RDF triples referring to some company-specific business ontology. Our approach is based on the novel idea of dynamic enforcement: we incrementally build dynamic filters for each process instance, based on the applicable access control policy and on the current prefix of the event stream. The implementation and performance validation of our solution is also presented.

Analysis of SAP Log Data Based on Network Community Decomposition

Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case.

Development of a method for the probabilistic inference of sequences of a business process activities to support the business process management

Models of temporal rules of execution of the business process actions were proposed for the use in absence in the process model of complete information on the reasons for execution of these actions caused by interference of the work executors. The rules are formed on the basis of analysis of the sequence of events in the business process log which makes it possible to determine temporal conditions and constraints on execution of the corresponding actions. The rule models can be applied as an element of knowledge representation for the process management system since they reflect experience of the business process execution recorded in the log. The use of rules allows one to limit the number of possible versions of execution of the business process taking into account its current state. As a result, the time of making decisions on the process management is reduced for the case of contradiction between the current version of the business process and the model.A new method of probabilistic inference was proposed that uses the presented rules to form new, admissible sequences of actions in an atypical situation that arose as a result of adjustment of the business process by its executors. The method applies knowledge representations based on the Markov logic network which makes it possible to arrange new sequences of actions according to the probability of their execution using weighed temporal rules. Use of a combination of rules for pairs of sequential and spaced in time actions ensures higher accuracy of calculating the probability of execution of new business process versions. The proposed method takes into account information from the event log when rules are supplemented. This enables continuous supplementing of rules in execution of the business process. The above enables practical real­time application of the method in automated formation and expansion of knowledge bases for the process management systems.

Development of knowledge representation based on markov logical networks in the business process mangement system

The problem of constructing knowledge representation in the process control system based on the analysis of the behavior of business processes, represented in the form of logs of events, is studied. Each event characterizes the action of the business process. The urgency of the problem is determined by the fact that when managing complex knowledge-capacious business processes, performers can change the sequence of actions taking into account additional knowledge about the subject area. As a result, there is a discrepancy between the process and its model, which creates difficulties for the further management of this business process. To eliminate this discrepancy, it is necessary to formalize the additional knowledge used and apply them in process management, which requires the creation of an appropriate knowledge representation. The proposed knowledge representation model takes into account the static and dynamic characteristics of the business process. The static characteristics of a business process are specified by facts and rules with arguments represented by the attributes of the log events. Facts and rules are formed on the basis of appropriate templates. Attributes specify the values of the properties of objects with which the business process operates. Dynamic features of the business process are determined through the current distribution of the probability that the rules will be executed, taking into account the attributes of the current business process log event. The proposed model is characterized by the fact that it takes into account the limitations on the permissible sequences of execution of the actions of the business process, as well as restrictions based on a priori knowledge of the subject area. Such restrictions will reduce the complexity of the problem of finding the probabilities of a successful completion of a business process by reducing the number of allowed trails in the event that the performers have changed the sequence of actions. In practical terms, the model provides the ability to support decision-making on the management of knowledge-intensive business processes based on predicting the probabilities of achieving the final state of the process, taking into account the attributes of log events.

Techniques of reordering traces in the event logs in business process management tasks

The problem of formation of input data for construction of models of business processes by methods of the intellectual analysis of processes is studied. It is shown that event logs of real information systems do not always meet the requirements of the ordering of input data on processes, that is, a log can have one track with events from different business processes. The tasks of analysis of trace characteristics in the business process event log and the development of methods for forming process events information order tracks organized on the basis of the comparison of event attributes and on the basis of comparison with the invariants of the event attributes of the business process logs are organized in the processes of the log of events events of the process management information system. The first method generates business process log lines based on pairwise comparison of event attributes based on the criterion for maximizing the number of shared attribute values for this pair. The method sequentially selects events from the common path for several business processes, and after pairwise comparison of the attributes with the events of the traces of specific business processes, determines the belonging of the next event to the path of the corresponding process. When comparing, the end event of the business process is also detected. The second method generates log lines based on the comparison of the invariant of the event attributes and the event, which is analyzed by the criterion of maximizing the weight of the values of the common attributes. Unlike the first method, when choosing a new business process, an invariant is created in the form of a sum of weights of the values of the attributes of the log of the business process. The scales of attribute values reflect the number of occurrences of these values in the execution of the business process. This allows you to take into account the history of the implementation of the business process in implementing the method. In practical terms, both proposed methods allow the formation of a set of business process event logs that are executed in parallel in a format suitable for use of methods and technologies of the intellectual analysis of proces ses. The first method has a lower accuracy. However, its advantage is the ability to use in the presence of only a joint path of several business processes, without the previously known orderly traces log of each business process. The second method allows you to increase the accuracy of the highlighting of the event path for each business process. The disadvantage of this method is that it requires a priori formation of the invariant of the attributes of the business process events.

Development of the representation of cause-effect dependencies for the knowledge base of the business process management system

The problem of constructing knowledge representation in the process control system for knowledge-capacious business processes in the aspect of reflecting the cause-effect relationships between the context of performance of actions and the actions of the business process is studied. It is shown that the general approach to solving this problem is related to the isolation of causal dependencies on the basis of an analysis of the event log containing records of the behavior of the business process. When solving the problem, problems of clarifying the structure of the representation of the cause-effect aspect of knowledge are formulated in accordance with the features of the log of the information system of process control; the construction of logical facts and rules in accordance with the structure of log events; formalization of knowledge representation taking into account facts, rules and a priori restrictions. It is established that the connection between the artifacts of the context and the events of the business process log is carried out through common attributes. Between artifacts and attributes and between events and attributes, there is one-to-many relationship. The logical component of the knowledge base is structured in the form of logical facts and rules. Logical facts determine the state of the business process at discrete points in time based on the values of the properties of artifacts. The output rules determine the change in the state of the business process. A knowledge representation is proposed that takes into account the state of the context for the execution of business process actions in the form of weighted logical facts, as well as output rules that support the selection of actions taking into account the current state of the context. The difference between the proposed representation is that when defining facts, the attributes of events are taken into account, and the rules are the structure and sequence of events of the business process log. A priori knowledge of the subject area in the form of constraints is also tak en into account. The practical importance of the knowledge representation is the ability to automatically identify the cause-effect relationships between the actions of the business process in accordance with the information in its log.

METHOD OF AUTOMATED CONSTRUCTION AND EXPANSION OF THE KNOWLEDGE BASE OF THE BUSINESS PROCESS MANAGEMENT SYSTEM

The problem of constructing and using the knowledge representation in the process control system is studied. It is shown that when implementing knowledge-intensive business process management, it is necessary to use automated construction and expansion knowledge base to support decision-making in accordance with the current state of the context for the implementation of business process actions. The state of the context is specified as a set of weighted logical facts, the arguments of which are the values of the attributes of the events of the business process log. The sequence of the process implementation at each moment of time is displayed in the form of a probabilistic distribution of the possible rules of executing the actions of the business process in this context. The method of automated construction and updating of the knowledge base of the information system of process control is proposed. The method includes the stages of forming knowledge representation templates, constructing context descriptions, logical facts, constructing rules, and calculating the probability distribution for rules. The method creates opportunities to support decision-making on the management of the business process in the event of a discrepancy between the current implementation of the business process and its model.

A Novel Business Process Prediction Model Using a Deep Learning Method

The ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.

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.

Efficiently interpreting traces of low level events in business process logs

Process mining methods have been proven effective in turning historical log data into actionable process knowledge. However, most of them work under the assumption that the events reported in the logs can be easily mapped to well-defined process activities, that are the terms in which analysts are used to reason on the processes’ behaviors. We here consider the challenging scenario where this assumption does not hold: the log traces are sequences of low-level operations with no explicit reference to the corresponding high-level process activities. In this setting, we face the fundamental problem of bringing the log traces to the abstraction level of the analyst’s perspective. Formally, given a trace Φ, and on the basis of a high-level behavioral description of the processes, we search for every possible interpretation⟨σ, W⟩ of Φ, where σ is a sequence of high-level activities whose execution may have generated the sequence of low-level operations Φ, and, in turn, W is a process that may have triggered the execution of σ. We address this problem probabilistically, and propose a framework that builds a compact representation of Φ’s interpretations, each associated with a probability score. This probability measures how likely the associated interpretation is the correct one, and it is evaluated adopting a revision paradigm guided by the background knowledge provided by the processes’ models. Notably, our approach can deal with “complex” activities (i.e., each generating a sequence of low-level operations, rather than a single one), and with the case that the traces encode process instances exhibiting some deviation from the expected behaviors encoded in the process models.