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.     

Using intentional fragments to bridge the gap between organizational and intentional levels

ContextBusiness process models provide a natural way to describe real-world processes to be supported by software-intensive systems. These models can be used to analyze processes in the system-as-is and describe potential improvements for the system-to-be. But, how well does a given business process model satisfy its business goals? How can different perspectives be integrated in order to describe an inter-organizational process?ObjectiveThe aim of the present paper is to link the local and the global perspectives of the inter-organizational business process defined in BPMN 2.0 (Business Process Model and Notation) to KAOS goal models (Keep All Objectives Satisfied). We maintain a separation of concerns between the intentional level captured by the goal model and the organizational level captured by the process model. The paper presents the concept of intentional fragment (a set of flow elements of the process with a common purpose) and assess its usefulness.MethodWe conducted empirical experiments where the proposed concepts – here the intentional fragments – are validated by users. Our method relies on an iterative improvement process led by users feedback.ResultsWe find that the concept of intentional fragment is useful for (1) analyzing the business process model (2) reasoning about the relations between the goal model and the business process model and (3) identifying new goals. In a previous work we focused on BPMN 2.0 collaboration models (local view). This paper extends the previous work by integrating the global view given by choreography models in the approach.ConclusionWe conclude that the notion of intentional fragment is a useful mean to relate business process models and goal models while dealing with their different nature (activity oriented vs goal oriented). Intentional fragments can also be used to analyze the process model and to infer new goals in an iterative manner.     

BPMNt: A BPMN extension for specifying software process tailoring

ContextAlthough SPEM 2.0 has great potential for software process modeling, it does not provide concepts or formalisms for precise modeling of process behavior. Indeed, SPEM fails to address process simulation, execution, monitoring and analysis, which are important activities in process management. On the other hand, BPMN 2.0 is a widely used notation to model business processes that has associated tools and techniques to facilitate the aforementioned process management activities. Using BPMN to model software development processes can leverage BPMN’s infrastructure to improve the quality of these processes. However, BPMN lacks an important feature to model software processes: a mechanism to represent process tailoring.ObjectiveThis paper proposes BPMNt, a conservative extension to BPMN that aims at creating a tailoring representation mechanism similar to the one found in SPEM 2.0.MethodWe have used the BPMN 2.0 extensibility mechanism to include the representation of specific tailoring relationships namely suppression, local contribution, and local replacement, which establish links between process elements (such as in the case of SPEM). Moreover, this paper also presents some rules to ensure the consistency of BPMN models when using tailoring relationships.ResultsIn order to evaluate our proposal we have implemented a tool to support the BPMNt approach and have applied it for representing real process adaptations in the context of an academic management system development project. Results of this study showed that the approach and its support tool can successfully be used to adapt BPMN-based software processes in real scenarios.ConclusionWe have proposed an approach to enable reuse and adaptation of BPMN-based software process models as well as derivation traceability between models through tailoring relationships. We believe that bringing such capabilities into BPMN will open new perspectives to software process management.     

An intelligent modeling and analysis method of manufacturing process using the first-order predicate logic

Manufacturing process refers to machining sequence from raw materials to final products. Process plan has important effects on manufacturing process. In general, process designer relies on his experience and knowledge to arrange the process plan. For a complex part, it takes long time and effort to determine process plan. In this paper, an intelligent modeling and analysis method using the first-order predicate logic is proposed to evaluate the manufacturing performance. First, the logic predicates used to represent the process plan are defined according to the machining methods, and the predicate variables are discussed in detail. Consequently, the process plan can be represented in the form of the first-order predicate logic. Second, a type of element model composed of four nodes and four links is put forward in order to construct the process model. All components in this element model are respectively explained, and the mapping relationship between element model and predicate logic is described in detail. According to engineering practices, logic inference rules are suggested and the inference process is illustrated. Hence, the manufacturing process model can be constructed. Third, the process simulation is carried out to evaluate the performance of manufacturing system by using measures such as efficiency, the machine utilization, etc. Finally, a case study is given to explain this intelligent modeling method using the first-order predicate logic.     

From fine-grained to abstract process models: A semantic approach

Organizations actively managing their business processes face a rapid growth of the number of process models that they maintain. Business process model abstraction has proven to be an effective means to generate readable, high-level views on business process models by showing coarse-grained activities and leaving out irrelevant details. In this way, abstraction facilitates a more efficient management of process models, as a single model can provide for many relevant views. Yet, it is an open question how to perform abstraction in the same skillful way as experienced modelers combine activities into more abstract tasks. This paper presents an approach that uses semantic information of a process model to decide on which activities belong together, which extends beyond existing approaches that merely exploit model structural characteristics. The contribution of this paper is twofold: we propose a novel activity aggregation method and suggest how to discover the activity aggregation habits of human modelers. In an experimental validation, we use an industrial process model repository to compare the developed activity aggregation method with actual modeling decisions, and observe a strong correlation between the two. The presented work is expected to contribute to the development of modeling support for the effective process model abstraction.     

基于GIS的空间过程模拟建模方法研究

ＧＩＳ空间过程建模分析功能是ＧＩＳ走向广泛应用的基础,为了实现这一目标,不公要求ＧＩＳ的数据模型能够充分表达过程模型所需的基本数据集和模拟模模型的输出数据集,还要求ＧＩＳ提供更加丰富的空间分析功能,尤其是多时相空间分析功能。     

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.     

How collaborative technology supports cognitive processes in collaborative process modeling: A capabilities-gains-outcome model

We examine which capabilities technologies provide to support collaborative process modeling. We develop a model that explains how technology capabilities impact cognitive group processes, and how they lead to improved modeling outcomes and positive technology beliefs. We test this model through a free simulation experiment of collaborative process modelers structured around a set of modeling tasks. With our study, we provide an understanding of the process of collaborative process modeling, and detail implications for research and guidelines for the practical design of collaborative process modeling.     

Modeling and monitoring of nonlinear multi-mode processes

In this paper, a new subspace separation method is proposed and a new modeling and monitoring approach in multi-mode processes based on subspace separation is proposed. The existing modeling methods have the following disadvantages: (1) different monitoring models are built in view of each different mode, which needs each mode to be able to offer fully complete modeling reference data. (2) The connection between each mode is ignored, which could be useful in process modeling and monitoring. The proposed method has the following advantages: (1) the common subspace is extracted and the monitoring performance of multi-mode processes is significantly improved. (2) New subspace separation is used to establish an integrated monitoring system, which would simplify the monitoring model structure and enhance its reliability. (3) The direct relationship of input dataset and output dataset is considered in the multi-mode processes, which is crucial for the complex industry process. Experiment results show effectiveness of the proposed method.     

Inductive process modeling

In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to learning process models from time-series data and illustrate its behavior in three domains. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem. Editor: David Page.     

Generative software product line development using variability-aware design patterns

Software Product Lines (SPLs) are an approach to reuse in-the-large that models a set of closely related software systems in terms of commonalities and variabilities. Design patterns are best practices for addressing recurring design problems in object-oriented source code. In the practice of implementing SPL, instances of certain design patterns are employed to handle variability, which makes these “variability-aware design patterns” a best practice for SPL design. However, currently there is no dedicated method for proactively developing SPLs using design patterns suitable for realizing variable functionality. In this paper, we present a method to perform generative SPL development with design patterns. We use role models to capture design patterns and their relation to a variability model. We further allow mapping of individual design pattern roles to (parts of) implementation elements to be generated (e.g., classes, methods) and check the conformance of the realization with the specification of the pattern. We provide definitions for the variability-aware versions of the design patterns Observer, Strategy, Template Method and Composite. Furthermore, we support generation of realizations in Java, C++ and UML class diagrams utilizing annotative, compositional and transformational variability realization mechanisms. Hence, we support proactive development of SPLs using design patterns to apply best practices for the realization of variability. We realize our concepts within the Eclipse IDE and demonstrate them within a case study.     

A knowledge transfer framework to support rapid process modeling in aerosol jet printing

Aerosol jet printing (AJP) technology recently gained considerable attention in an electronic manufacturing industry due to its ability to fabricate parts with fine resolution and high flexibility. However, morphology control has been identified as the main limitation of AJP process, which drastically affects the electrical performance of printed components. Even though previous researches have made significant efforts in process modeling to improve the controllability of the the printed line morphology, the modeling process is still inefficient under modified operating conditions due to the repeated experiments. In this paper, a knowledge transfer framework is proposed for efficient modeling of the AJP process under varied operating conditions. The proposed framework consists of three critical steps for rapid process modeling of AJP. First, a sufficient source domain dataset at a certain operating condition is collected to develop a source model based on Gaussian process regression. Then, the representative experimental points are selected from the source domain to construct a target dataset under different operating conditions. Finally, classical knowledge transfer approaches are adopted to extract the built-in knowledge from the source model; thus, a new process model can be developed efficiently by the transferred knowledge and the representative dataset from the target domain. The validity of the proposed framework for the rapid process modeling of AJP is investigated by case study, and the limitations of the classical knowledge transfer approaches adopted in AJP are also analyzed systematically. The proposed framework is developed based on the principles of knowledge discovery, which is different from traditional process modeling approaches in AJP. Therefore, the modeling process is more systematic and cost-efficient, which will be helpful to improve the controllability of the line morphology. Additionally, due to its data-driven based characteristics, the proposed framework can be applied to other additive manufacturing technologies for process modeling researches.     

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

Based on the structure of process models a hierarchically structured state-space model has been proposed for process networks with controlled mass convection and constant physico-chemical properties. Using the theory of cascade-connected nonlinear systems and the properties of Metzler and Hurwitz matrices it is shown that process systems with controlled mass convection and without sources or with stabilizing linear source terms are globally asymptotically stable. The hierarchically structured model gives rise to a distributed controller structure that is in agreement with the traditional hierarchical process control system structure where local controllers are used for mass inventory control and coordinating controllers are used for optimizing the system dynamics. The proposed distributed controller is illustrated on a simple non-isotherm jacketed chemical reactor.     

Team mental models: techniques, methods, and analytic approaches

Effective team functioning requires the existence of a shared or team mental model among members of a team. However, the best method for measuring team mental models is unclear. Methods reported vary in terms of how mental model content is elicited and analyzed or represented. We review the strengths and weaknesses of vatrious methods that have been used to elicit, represent, and analyze individual and team mental models and provide recommendations for method selection and development. We describe the nature of mental models and review techniques that have been used to elicit and represent them. We focus on a case study on selecting a method to examine team mental models in industry. The processes involved in the selection and development of an appropriate method for eliciting, representing, and analyzing team mental models are described. The criteria for method selection were (a) applicability to the problem under investigation; (b) practical considerations - suitability for collecting data from the targeted research sample; and (c) theoretical rationale - the assumption that associative networks in memory are a basis for the development of mental models. We provide an evaluation of the method matched to the research problem and make recommendations for future research. The practical applications of this research include the provision of a technique for analyzing team mental models in organizations, the development of methods and processes for eliciting a mental model from research participants in their normal work environment, and a survey of available methodologies for mental model research.     

Structuring acyclic process models

This article studies the problem of transforming a process model with an arbitrary topology into an equivalent well-structured process model. While this problem has received significant attention, there is still no full characterization of the class of unstructured process models that can be transformed into well-structured ones, nor an automated method for structuring any process model that belongs to this class. This article fills this gap in the context of acyclic process models. The article defines a necessary and sufficient condition for an unstructured acyclic process model to have an equivalent well-structured process model under fully concurrent bisimulation, as well as a complete structuring method. The method has been implemented as a tool that takes process models captured in the BPMN and EPC notations as input. The article also reports on an empirical evaluation of the structuring method using a repository of process models from commercial practice.     

Automating data exchange in process choreographies

Communication between organizations is formalized as process choreographies in daily business. While the correct ordering of exchanged messages can be modeled and enacted with current choreography techniques, no approach exists to describe and automate the exchange of data between processes in a choreography using messages. This paper describes an entirely model-driven approach for BPMN introducing a few concepts that suffice to model data retrieval, data transformation, message exchange, and correlation – four aspects of data exchange. For automation, this work utilizes a recent concept to enact data dependencies in internal processes. We present a modeling guideline to derive local process models from a given choreography; their operational semantics allows to correctly enact the entire choreography from the derived models only including the exchange of data. Targeting on successful interactions, we discuss means to ensure correct process choreography modeling. Finally, we implemented our approach by extending the camunda BPM platform with our approach and show its feasibility by realizing all service interaction patterns using only model-based concepts.     

Configuration vs. adaptation for business process variant maintenance: An empirical study

Many approaches for process variant management employ a reference model for deriving a target variant either using configuration or adaptation mechanisms. What is missing at this stage is empirical insight into their relative strengths and weaknesses. Our paper addresses this gap. We selected C-YAWL and vBPMN for a comparative, empirical user study. Both approaches center on a reference process, but provide different types of configuration and adaptation mechanisms as well as modularization support. Along with this aspect, we investigate the effect of model complexity and professional level on human process variant modeling performance. Given unlimited processing time, we could not show that complexity or the participant's professional level significantly impacts the task success rate or user contentment. Yet, an effect of model complexity can be noted on the execution speed for typical variant maintenance tasks like the insertion and deletion of process steps. For each of the performance measures of success rate, user contentment and execution speed, vBPMN performs significantly better than C-YAWL. We argue that this is due to vBPMN's advanced modularization support in terms of pattern-based process adaptations to construct process variants. These insights are valuable for advancing existing modeling approaches and selecting between them.     

Processing intrusion detection alert aggregates with time series modeling

The main use of intrusion detection systems (IDS) is to detect attacks against information systems and networks. Normal use of the network and its functioning can also be monitored with an IDS. It can be used to control, for example, the use of management and signaling protocols, or the network traffic related to some less critical aspects of system policies. These complementary usages can generate large numbers of alerts, but still, in operational environment, the collection of such data may be mandated by the security policy. Processing this type of alerts presents a different problem than correlating alerts directly related to attacks or filtering incorrectly issued alerts.We aggregate individual alerts to alert flows, and then process the flows instead of individual alerts for two reasons. First, this is necessary to cope with the large quantity of alerts – a common problem among all alert correlation approaches. Second, individual alert’s relevancy is often indeterminable, but irrelevant alerts and interesting phenomena can be identified at the flow level. This is the particularity of the alerts created by the complementary uses of IDSes.Flows consisting of alerts related to normal system behavior can contain strong regularities. We propose to model these regularities using non-stationary autoregressive models. Once modeled, the regularities can be filtered out to relieve the security operator from manual analysis of true, but low impact alerts. We present experimental results using these models to process voluminous alert flows from an operational network.     

A graph-theoretic method for the inductive development of reference process models

Business process management is one of the most widely discussed topics in information systems research. As process models advance in both complexity and maturity, reference models, serving as reusable blueprints for the development of individual models, gain more and more importance. Only a few business domains have access to commonly accepted reference models, so there is a widespread need for the development of new ones. This article describes a new inductive approach for the development of reference models, based on existing individual models from the respective domain. It employs a graph-based paradigm, exploiting the underlying graph structures of process models by identifying frequent common subgraphs of the individual models, analyzing their order relations, and merging them into a new model. This newly developed approach is outlined and evaluated in this contribution. It is applied in three different case studies and compared to other approaches to the inductive development of reference models in order to highlight its characteristics as well as assets and drawbacks.