Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn business process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. The proposed approach allows us to simulate different types of changes, including the addition of new activity types to a process. This latter capability is achieved by encoding the activities by means of embeddings, rather than representing them as one-hot-encoded categories. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches. The evaluation also sheds light into the relative performance of multiple embedding approaches to represent the activities.

Abstract Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.

Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains.

This paper presents MONO, a tool to support interdisciplinary and geographically dispersed teams. MONO supports the definition, execution, and monitoring of collaborative processes to allow development engineers to interact with musicians, designers, and producers, to create digital contents such as video games or animated short films. MONO supports geographically distributed teams, involved in digital content software development projects, by means of dynamic and configurable executable processes, and coordination and monitoring tools. In this work, we describe our experience developing MONO and the results obtained in our initial validation.

Software Product Line Engineering has introduced feature modeling as a domain analysis technique used to represent the variability of software products and decision-making scenarios. We present a model-based transformation approach to solve conflicts among configurations performed by different stakeholders on feature models. We propose the usage of a domain-specific language named CoCo to specify attributes as non-functional properties of features, and to describe business-related decision rules in terms of costs, time, and human resources. These specifications along with the stakeholders' configurations and the feature model are transformed into a constraint programming problem, on which decision rules are executed to find a non-conflicting set of solution configurations that are aligned to business objectives. We evaluate CoCo's compositionality and model complexity simplification while using a set of motivating decision scenarios.

Recently, there has been a big movement towards converging engineering education and practice by means of defining clear and complete program goals. In this context, a large scale of educational institutions face challenges on how to specify the desired educational goals for a specific discipline and on how to instantiate and evaluate their program-specific designs according to these goals. We have defined a curricula design model for designing, redesigning, and evaluating systems and computing engineering (S&C) programs. The core of this model is the orientation to projects for the definition of educational goals that facilitate the definition of courses that integrate a set of these desired goals. This paper presents the core elements of the curricula design model and the methodology to instantiate it into a new program design that satisfies a desired professional profile. The curricula design model has been validated through its instantiation for redesigning the S&C program at the University of Los Andes in Colombia.

Workflow monitoring and analysis concerns aim at identifying potential improvements of workflow applications. This paper presents an approach to specify and implement monitoring and analysis concerns on workflow applications raising the level of abstraction for workflow analysts. First, the specification of monitoring and analysis concerns is declared in a technology-independent way with a domain-specific language named MonitA. MonitA makes extensive use of the data available in the workflow application and its constituents to enhance the monitoring and analysis specifications. Second, we defined and implemented a strategy to assist developers to enhance a given workflow technology to support the generation of the monitoring and analysis code and its composition with the workflow application. This instrumentation-based approach enables the monitoring and analysis of workflow applications during their operational execution. We illustrate the flexibility of our approach by targeting different workflow platforms and different workflow applications.

In workflow management systems, analysis concerns related to monitoring, measurement, and control aim at identifying potential improvements of workflow applications. However, the specification of analysis concerns is done using a specific workflow language and engine, producing entangled code which is detrimental to their maintainability. The purpose of this paper is twofold. First, it presents briefly a domain-specific language to specify analysis concerns, independently of any workflow technology and in a modularized way. Second, it shows a strategy to assist developers to enhance a given workflow technology to support the automated implementation of analysis concerns into its workflow applications. Thus, given a workflow application and its analysis concerns, they are automatically integrated producing an enhanced executable workflow application.