Mining business process variants: Challenges, scenarios, algorithms

During the last years a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. Respective model adaptations result in a large number of model variants that are derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. In this paper we address two scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first one is characterized by a reference process model and a collection of related process variants. The goal is to improve the original reference process model such that it fits better to the variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to “merge” these variants into a new reference process model. We suggest two algorithms that are applicable in both scenarios, but have their pros and cons. We provide a systematic comparison of the two algorithms and further contrast them with conventional process mining techniques. Comparison results indicate good performance of our algorithms and also show that specific techniques are needed for learning from process configurations and adaptations. Finally, we provide results from a case study in automotive industry in which we successfully applied our algorithms.     

Plural: A decentralized business process modeling method

Top-down and centralized approaches prevail in the design and improvement of business processes. However, centralized structures pose difficulties for organizations in adapting to a rapidly changing business environment. Here we present the Plural method which can be used to guide organizations in performing process modeling in a decentralized way. Instead of a centralized group of people understanding, modeling and improving processes, our method allows individuals to model and improve their own processes to help in fulfilling their roles in the organization. An individual model depicts a set of activities performed by a role, which together result in a cohesive service within the organization. These individual models are then integrated as necessary to show the way the organization works. We applied the Plural method in a case study of a small-size software organization. We describe the method and its underlying principles and then discuss the findings of our case study, lessons learned, and limitations. The study thus provided evidence of Plural's utility and showed how an organization might exploit its strengths.     

The generic model query language GMQL – Conceptual specification,implementation, and runtime evaluation

The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model fragment to be returned. In this paper, we derive requirements for a generic model query language from the literature and formally specify the language’s syntax and semantics. We conduct an analysis of GMQL׳s theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research.     

Time prediction based on process mining

Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g., the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between “overfitting” and “underfitting”. The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability.     

A general process mining framework for correlating,predicting and clustering dynamic behavior based on event logs

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands.     

Ensuring correctness during process configuration via partner synthesis

Variants of the same process can be encountered within one organization or across different organizations. For example, different municipalities, courts, and rental agencies all need to support highly similar processes. In fact, procurement and sales processes can be found in almost any organization. However, despite these similarities, there is also the need to allow for local variations in a controlled manner. Therefore, many academics and practitioners have advocated the use of configurable process models (sometimes referred to as reference models). A configurable process model describes a family of similar process models in a given domain. Such a model can be configured to obtain a specific process model that is subsequently used to handle individual cases, for instance, to process customer orders. Process configuration is notoriously difficult as there may be all kinds of interdependencies between configuration decisions. In fact, an incorrect configuration may lead to behavioral issues such as deadlocks and livelocks. To address this problem, we present a novel verification approach inspired by the “operating guidelines” used for partner synthesis. We view the configuration process as an external service, and compute a characterization of all such services which meet particular requirements via the notion of configuration guideline. As a result, we can characterize all feasible configurations (i.e., configurations without behavioral problems) at design time, instead of repeatedly checking each individual configuration while configuring a process model.     

Water resources of Cyprus under changing climatic conditions: Modelling approach,validation and limitations

We examine trends in the water resources of Cyprus by focussing on water flux changes in the important Kouris catchment. Our modelling approach is general and is a synthesis of an adapted conceptual daily rainfall-runoff model, radiation transfer models that use high resolution MODIS satellite climatological data and GCM scenarios for future climatic change. We used climatic data as input to our models, downscaled to the catchment resolution from two climate scenarios: the mild RCP2.6 and the extreme RCP8.5, to estimate water resources by the end of the 21st century. The models show that the present mean annual rainfall resource of 174 Mm³ will be reduced to 162 Mm³ and 132 Mm³, for the mild and extreme scenario, respectively. The present mean discharge of 21.5 Mm³ into the Kouris dam from the catchment will decrease to 16.6 Mm³ and 6.9 Mm³ under the mild and extreme scenario, respectively.     

Process improvement: an alternative to BPR for software development organizations

Whereas BPR (Business Process Re-engineering or Business Process Re-design) is being offered as a means by which organizations may be able to respond to the challenge of increased competition, Process Improvement, on the other hand aims to improve productivity, product quality and competitive advantage. Fundamentally, both activities offer a means by which organizations may be able to deal with a rapidly changing business environment, through internal change. BPR extends the concept of process change (albeit rapid change through innovation) to include changes to the basic assumptions and principles of management. The results, however are not positive: estimates of between 50 and 70% of firms fail to achieve the expected dramatic gains from BPR. An alternative to BPR for software development organizations is Process Assessment and Process Improvement. This paper presents a Process/Product Quality Model which incorporates three separate evaluations: process conformance, process effectiveness and product quality as a means of evaluating what is, i.e. the current status of activities, within a software development organization. The model aims to identify possible areas for process improvement and to uncover all relevant issues which may affect attempts to implement improvement initiatives. Preliminary findings from a set of case studies conducted within software development organizations in Australia are presented in support of the usefulness of the model.     

Conformance checking of processes based on monitoring real behavior

Many companies have adopted Process-aware Information Systems (PAIS) to support their business processes in some form. On the one hand these systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. On the other hand explicit process models describing how the business process should (or is expected to) be executed are frequently available. Together with the data recorded in the log, this situation raises the interesting question “Do the model and the log conform to each other?”. Conformance checking, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log, and their quantification by the formation of metrics. This paper proposes an incremental approach to check the conformance of a process model and an event log. First of all, the fitness between the log and the model is measured (i.e., “Does the observed process comply with the control flow specified by the process model?”). Second, the appropriateness of the model can be analyzed with respect to the log (i.e., “Does the model describe the observed process in a suitable way?”). Appropriateness can be evaluated from both a structural and a behavioral perspective. To operationalize the ideas presented in this paper a Conformance Checker has been implemented within the ProM framework, and it has been evaluated using artificial and real-life event logs.     

小型软件开发单位软件过程改进模型探讨

如何在小型软件组织中采用过程改进模型CMM和ISO9001已经成为软件工业的一种挑战。指出小组织所面临的困难,提出了解决问题的建议,同时给出了小型软件组织的过程改进模型。     

Methodological issues in product evaluation: the influence of testing environment and task scenario

This article examines the utility of two commonly used approaches in the evaluation of interactive consumer products: lab-based testing and single task scenarios. These are compared to two more complex and resource-demanding approaches (field-based testing and dual task scenarios) with regard to the test results they produce. An experiment with N = 80 users was carried out, employing a 2 (laboratory vs. field) by 2 (single task vs. dual task scenario) by 2 (on-product information: present vs. absent) between-subjects design. On-product information (advising users to save water and electricity during kettle usage) represented the intervention, of which the effects on user behaviour were compared under the different experimental conditions. The main finding was that the impact of on-product information on user behaviour was strongest in the lab-based testing environment using a single task scenario (i.e., most economical testing condition), compared to the three other experimental conditions. The work found similar effects for self-report measures. The findings of the study point to the risk that the effects of system redesign on user behaviour may be overestimated if low-fidelity testing approaches are employed. The relevance of these findings for other application areas is also discussed (e.g., design of warnings).     

Models,metrics, and management of IS development

Two types of models can assist the information system manager in gaining greater insight into the system development process. They are: isomorphic models that represent cause-effect relationships between certain conditions (e.g., structured techniques) and certain observable states (e.g., productivity change); and paramorphic models that describe an outcome but do not describe the processes or variables that influence the outcome (e.g., estimation of project time or cost). The two models are shown to be interrelated since the relationships of the first model are determinants of the parameters of the second model.IS managers can make significant contributions by developing isomorphic models tailored to their own organizations. However, metrics that measure relevant characteristics of programs and systems are required before substantial progress can be made. Although some initial attempts have been made to develop metris for program quality, program complexity, and programmer skill, much more work remains to be done. In addition, other metries must be developed that will require the involvement of personnel, not only in the computer sciences, but also in information systems, the behavioral sciences, and IS management.     

Towards an advanced model-driven engineering toolbox

Model-driven engineering (MDE) is frequently presented as an important change in software development practice. However, behind this new trend, one may recognize a lot of different objectives and solutions. This paper first studies the multiple facets of MDE and its evolution in the recent period. Several new usage scenarios (i.e., reverse engineering, and models at runtime), which have appeared following the initial forward engineering scenario, i.e., platform-independent model (PIM) to platform-specific model (PSM), are described. Not surprisingly, new applications trigger the need for new tools and the requirement for model engineering platforms evolves correspondingly. We have adapted the AmmA toolbox to these new usages and the result is described herein together with some illustrative examples.     

Business alignment: using process mining as a tool for Delta analysis and conformance testing

Increasingly, business processes are being controlled and/or monitored by information systems. As a result, many business processes leave their “footprints” in transactional information systems, i.e., business events are recorded in so-called event logs. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge. In this paper we focus on the potential use of process mining for measuring business alignment, i.e., comparing the real behavior of an information system or its users with the intended or expected behavior. We identify two ways to create and/or maintain the fit between business processes and supporting information systems: Delta analysis and conformance testing. Delta analysis compares the discovered model (i.e., an abstraction derived from the actual process) with some predefined processes model (e.g., the workflow model or reference model used to configure the system). Conformance testing attempts to quantify the “fit” between the event log and some predefined processes model. In this paper, we show that Delta analysis and conformance testing can be used to analyze business alignment as long as the actual events are logged and users have some control over the process.

Semi-automatic inductive construction of reference process models that represent best practices in public administrations: A method

Business process management often uses reference models to improve processes or as starting point when creating individual process models. The current academic literature offers primarily deductive methods with which to develop these reference models, although some methods develop reference models inductively from a set of individual process models, focusing on deriving and representing common practices. However, there is no inductive method with which to detect best practices and represent them in a reference model. This paper addresses this research gap by proposing a method by which to develop reference process models that represent best practices in public administrations semi-automatically and inductively. The method uses a merged model that retains the structure of the source models while detecting their common parts. It identifies best practices using query constructs and ranking criteria to group the source models’ elements and to evaluate these groups. We provide a conceptualization of the method and demonstrate its functionality using an artificial example. We describe our implementation of the method in a software prototype and report on its evaluation in a workshop with domain and method experts who applied the method to real-world process models.     

Exploring how to use scenarios to discover requirements

This paper investigates the effectiveness of different uses of scenarios on requirements discovery using results from requirements processes in two projects. The first specified requirements on a new aircraft management system at a regional UK airport to reduce its environmental impact. The second specified new work-based learning tools to be adopted by a consortium of organizations. In both projects scenarios were walked through both in facilitated workshops and in the stakeholders’ workplaces using different forms of a scenario tool. In the second project, scenarios were also walked through with a software prototype and creativity prompts. Results revealed both qualitative and quantitative differences in discovered requirements that have potential implications for models of scenario-based requirements discovery and the design of scenario tools.