Conformance Checking Research Articles

Business process mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.

Staged Symbolic Execution for Parallel Property Checking

While annotating functional correctness properties of code, e.g., using assertions, enables automated conformance checking of program behaviors to expected properties and has found its use in many bug finding techniques, efficiently checking properties in practice remains challenging. In this paper, we introduce a novel approach to distributing checking properties using staged symbolic execution, which consists of two stages: (1) the first stage searches for all feasible paths to the checked properties as targets for the second stage, and (2) the second stage distributes property checking along these target paths among multiple workers, each focusing on one or more paths depending on the available computing resources. We implement our approach on top of Symbolic PathFinder, and our experience with our approach in checking assertions annotated in two Java programs shows its effectiveness

Efficient Alignment Between Event Logs and Process Models

The aligning of event logs with process models is of great significance for process mining to enable conformance checking, process enhancement, performance analysis, and trace repairing. Since process models are increasingly complex and event logs may deviate from process models by exhibiting redundant, missing, and dislocated events, it is challenging to determine the optimal alignment for each event sequence in the log, as this problem is NP-hard. Existing approaches utilize the cost-based A* algorithm to address this problem. However, scalability is often not considered, which is especially important when dealing with industrial-sized problems. In this paper, by taking advantage of the structural and behavioral features of process models, we present an efficient approach which leverages effective heuristics and trace replaying to significantly reduce the overall search space for seeking the optimal alignment. We employ real-world business processes and their traces to evaluate the proposed approach. Experimental results demonstrate that our approach works well in most cases, and that it outperforms the state-of-the-art approach by up to 5 orders of magnitude in runtime efficiency.

Conformity checking of LPG Transportation Trucks by Modelling and Simulation

LPG Loading /unloading site is considered as a dangerous environment of significant risk, especially during LPG transfer operations. These risks may cause catastrophic dangers such as BLEVE (Boiling Liquid Expanding Vapour Explosion), UVCE (Unconfined Vapour Cloud Explosion), etc. The site consists of a tank (RST) containing LPG and threetransfer posts. Two posts allow loading of small carrier tankers 6 tons. The other one transfer post transfer LPG contained injumbo tankers 20 tons inside the tank. The industrial site at risks chooses to demonstrate the need for a modelling-simulation approach. The aim is to check first compliance and authorization of the truck when it enters the site for loading /unloading, to verify the authorization to make the transfer, and then, to identify the driver and his training to ensure that the transfer operations are going to take place without incident. Sometimes, it may happen that Consignment Operator (CO) is busy orabsent and thus the driver operates alone without being authorized, in this case the site risks a loss of control due to a lack of resources. The modelling-simulation will be done using multi-agent systems that will present the drivers, trucks, PO and loading unloading posts as agents in order to have a model facilitating this checking.

Process Discovery from Dependence-Complete Event Logs

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.

Conformance checking based on multi-perspective declarative process models

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behavior provided in the form of a process model. Recently, techniques for conformance checking based on declarative specifications have been developed. Such specifications are suitable to describe processes characterized by high variability. However, an open challenge in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. This means that declarative models used for conformance checking should not only describe the process behavior from the control flow point of view, but also from other perspectives like data or time. In this paper, we close this gap by presenting an approach for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented using artificial and real-life event logs.

Aligning observed and modelled behaviour based on workflow decomposition

ABSTRACTWhen business processes are mostly supported by information systems, the availability of event logs generated from these systems, as well as the requirement of appropriate process models are increasing. Business processes can be discovered, monitored and enhanced by extracting process-related information. However, some events cannot be correctly identified because of the explosion of the amount of event logs. Therefore, a new process mining technique is proposed based on a workflow decomposition method in this paper. Petri nets (PNs) are used to describe business processes, and then conformance checking of event logs and process models is investigated. A decomposition approach is proposed to divide large process models and event logs into several separate parts that can be analysed independently; while an alignment approach based on a state equation method in PN theory enhances the performance of conformance checking. Both approaches are implemented in programmable read-only memory (ProM). The correctness and effectiveness of the proposed methods are illustrated through experiments.

A Repair of Workflow Models Based on Mirroring Matrices

Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose the differences between observed and modeled behaviors. Some existing conformance checking techniques have been used for repairing a process model, but there are also many techniques can uncover these differences efficiently. So we can use these techniques to repair a process model. In this paper we research the problem of repairing the workflow models w.r.t. the corresponding real process has been changed and the change is adding new activities. To solve this problem, a mirroring matrix is presented based on the variant of footprints and parallel programs. The mirroring matrix is a uniform standard to compare an original model with a complete event log gained from the corresponding real process. Based on this information, the differences between a model and a log can be found. We suggest four algorithms that make model repair realized. Finally, the effectiveness and correctness of the proposed techniques are illustrated via contrastive experiments and analysis.

The Design of a Methodology for the Justification and Implementation of Process Mining

Process mining techniques allow organizations to extract knowledge from information systems that store process-related data. Process models can be (re)constructed based on process executions, the SOLL and IST position of process executions can be compared, processes can be bench-marked and simulated, and lastly, real-time operational support is provided through detection, prediction and recommendations. The value organizations can derive from these techniques is largely contingent upon the quality of their process data.Financial specialists are increasingly relying on information technology to support them in their jobs. Process mining yields greater visibility of operations. This paves the way for improved governance and control measures. Conformance checking supports internal and external auditors, compliance specialists, controllers and other related professions through testing whether the process is executed in accordance with the business rules. Process improvement specialists (e.g. lean six sigma professionals) can simulate changes in the process, to test how these changes affect the process. Detection can be used as a signaling tool, allowing organizations to take preventive or corrective measures. Prediction can be used to predict future related process parameters (e.g. workload). Lastly, recommendations provide operational employees with advice based on historic process executions.Van der Aalst (2011b) states the first step in any process mining project is its justification. However, neither the literature review, nor backward searches, provided methods for the justification and implementation of process mining projects. Therefore, this paper develops a method for the justification and implementation of process mining in organizations. A generic process mining business case framework is developed, which organizations can use as a guideline for developing their business case. Additionally, an eight phase methodology is developed, to assist organizations from their early planning stages up until reviewing the implementation.

Improving process conformance with Industry Foundation Processes (IFP)

Conformance with standard corporate and institutional processes and industry best practices are sought because of regulatory requirements and evidence that best practices lead to improved project performance. Automated workflow engine enabled Industry Foundation Processes (IFP) are introduced in this paper that facilitate process conformance through structural transparency, foundation process inheritance, and automated conformance checking. While IFP processes can be customized to suit particular project or corporate conditions, they need to conform to a standard core structure and thus behavior. This has been achieved through defining specific workflow inheritance rules and developing an automated structural process conformance checking algorithm. The algorithm has been developed based on graph theory fundamentals using a first-order logic language, which compares two workflows and detects the conformance of a customized one with its associated IFP. The developed algorithm has been functionally tested and validated with different structural settings of workflows with a number of critical cases. Its functionality has been demonstrated in this paper with an example of the commonly used process of RFI (Request for Information). A new construct is thus contributed that can help improve process conformance to industry best practices particularly in the architectural, engineering, and construction industry, leading to improved project conformance.

Does this fault lead to failure? Combining refinement and input–output conformance checking in fault-oriented test-case generation

In this paper we describe an advanced test-case generation technique that is implemented in our model-based test-case generator MoMuT::UML. The tool injects faults into a UML model and analyses if the faults propagate to the interface. If a fault does propagate to an observable failure, an explaining sequence of events is generated and converted into a test-case scenario. The faults are detected using a highly optimised refinement checker, their propagation is analysed with an input–output conformance (ioco) checker. We show that this combination is faster than pure input–output conformance checking. It has been used in a recent industrial application of testing automotive measurement devices. The refinement and ioco checker are implemented in Prolog using the SMT solver Z3.

Symbolic Input-Output Conformance Checking for Model-Based Mutation Testing

This paper presents an approach to use symbolic input output conformance checking for mutation-based test case generation. In this approach, a possibly non-deterministic action system model is used as basis for generating a number of mutants. Subsequently after the generation of mutants, the original model and the mutants are simultaneously symbolically executed and tested for conformance. Distinguishing test cases are generated, if non-conformance is detected during this process. Several optimisations of the conformance check are presented and their effectiveness is underpinned by listing experimental results.

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today׳s economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking (how to assess the conformance of the schedule and the actual process execution) and performance improvement (how to improve the operational process performance). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%.

Architecture Conformance Checking in Dynamically Typed Languages.

Architectural erosion is a recurrent problem faced by software architects, which might be even more severe in systems implemented in dynamically typed languages. The reasons are twofold: (i) some features provided by such languages make developers more propitious to break the planned architecture (e.g., dynamic invocations and buildings), and (ii) the developers’ community lacks tool support for monitoring the implemented architecture. To address these shortcomings, this paper presents an architectural conformance and visualization approach based on static code analysis techniques and on a lightweight type propagation heuristic. The central idea is to provide the developers’ community with means to control the architectural erosion process by reporting architectural violations and visualizing them in high-level architectural models, such as reflexion models and DSMs. This paper also describes a tool—called ArchRuby—that implements our approach. We evaluate our solution in three real-world systems identifying 48 architectural violations of which the developers had no prior knowledge. We also measure the effectiveness of our type propagation heuristic reporting that (i) the number of analyzed types raises 5% on the average and (ii) certain violations are only detected due to our heuristic.

Model-based incremental conformance checking to enable interactive product configuration

ContextModel-based product line engineering (PLE) is a paradigm that can enable automated product configuration of large-scale software systems, in which models are used as an abstract specification of commonalities and variabilities of products of a product line. ObjectiveIn the context of PLE, providing immediate feedback on the correctness of a manual configuration step to users has a practical impact on whether a configuration process with tool support can be successfully adopted in practice. MethodIn an existing work, a UML-based variability modeling methodology named as SimPL and an interactive configuration process was proposed. Based on the existing work, we propose an automated, incremental and efficient conformance checking approach to ensure that the manual configuration of a variation point conforms to a set of pre-defined conformance rules specified in the Object Constraint Language (OCL). The proposed approach, named as Zen-CC, has been implemented as an integrated part of our product configuration and derivation tool: Zen-Configurator. ResultsThe performance and scalability of Zen-CC have been evaluated with a real-world case study. Results show that Zen-CC significantly outperformed two baseline engines in terms of performance. Besides, the performance of Zen-CC remains stable during the configuration of all the 10 products of the product line and its efficiency also remains un-impacted even with the growing product complexity, which is not the case for both of the baseline engines. ConclusionThe results suggest that Zen-CC performs practically well and is much more scalable than the two baseline engines and is scalable for configuring products with a larger number of variation points.

A Conformity Check and Optimization of the Energy Management System Based on ISO 50001

The term energy management from the past few years is becoming increasingly important. Not only the trend toward clean energy sources triggered a process of rethinking in the minds of society but also pushed the politics to create incentives for companies in order to make them reduce their carbon footprint. The efficient use of energy can be described as an integral part of current political, social and economic decision making.From a business perspective energy efficient production is no longer just considered under the ecological aspect. Through high taxes, huge energy costs and steadily increasing energy prices, there is for companies in the relatively young field of energy management enormous potential to financially improve and consequently save costs sustainably. Furthermore starting from 2015 the energy peak equalization law is made stricter, requiring companies to pay even more in taxes if they fail to fulfill all the requirements set by the politics.Hence the role of energy management systems (EnMS), which deal with the continuous and systematic improvement of the energy performance of businesses, is becoming increasingly important.This paper will deal with an energy management system that was developed by the Institute for factory automation and production systems at Friedrich-Alexander-University Erlangen-Nürnberg. The paper audits the proposed concept and checks it for consistency with the international energy management norm ISO 50001. Additionally all the corrections and necessary optimizations are made in order to provide the companies with a complete energy management system.The aim of this paper is to verify and check the EnMS concept which was developed in the institute for factory automation and production systems (FAPS) for its conformity with the International ISO 50001 standard. Thus ensuring the long term certification according to DIN ISO 50001.

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, since the control flow perspective can be integrated with data and resource perspectives discovered from event logs. 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. Thus, on the basis of these conversion algorithms low-level models (such as Petri nets, causal nets and process trees) discovered from event logs using existing approaches can be represented in terms of BPMN. Moreover, we establish behavioral relations between Petri nets and BPMN models and use them to adopt existing conformance checking and performance analysis techniques in order to visualize conformance and performance information within a BPMN diagram. 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 complicated BPMN models in comparison with the initial models are identified.

Scientific workflows for process mining: building blocks, scenarios, and implementation

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.

DOES YOUR EVENT LOG FIT THE HIGH-LEVEL PROCESS MODEL?

Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. In this paper we consider the problem of models and event logs conformance checking. Conformance checking is intensively studied in the frame of process mining research, but only models and event logs of the same granularity were considered in the literature. Here we present and justify the method of checking conformance between a high-level model (e.g. built by an expert) and a low-level log (generated by a system). The article is published in the author’s wording.

A multi-criteria methodology to evaluate the optimal location of a multifunctional railway portal on the railway network

The installation of a multifunctional railway portal (or TCCS – Train Conformity Check system) can contribute to improve the safety of a railway infrastructure. The TCCS can detect the conformity of the trains traveling along the tracks, and can transfer the status information to a main traffic control center. This paper proposes a methodological approach based on Analytic Hierarchy Process (AHP) to evaluate the optimal locations to install a TCCS on a railway section. The eligibility and ranking of the potential sites have been defined with respect to constraints related to the rail line track layout and geometry, the TCCS technological features, and the required safety distance allowing the train to stop. The proposed approach has been applied to a real case study on the Italian railway.