Patterns to Enable Mass-Customized Business Process Monitoring

Mass-customization challenges the one-size-fits-all assumption of mass production, allowing customers to specify the options that best fit their requirements when choosing a product or a service. In business process management, to achieve mass-customization, providers offer to their customers the opportunity to customize the way in which a process will be enacted. We focus on monitoring as a specific customization aspect. We propose a multidimensional classification of modeling patterns for customized monitoring infrastructures. Patterns enable the provider to offer a set of customizable options to customers and design a monitoring infrastructure that fits the preferences specified by customers on such options. An example in the online advertising industry demonstrates how our framework can improve the services currently offered by providers

Practice-Driven Research on Enterprise Transformation

Item does not contain fulltextPractice-Driven Research on Enterprise Transformation : 4th Working Conference, PRET 2012 Gdansk, Poland, June 27, 201

Efficient discovery of understandable declarative process models from event logs

Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare models based on event logs. A Declare model is composed of temporal constraints. Despite the suitability of declarative process models for less structured processes, their discovery is far from trivial. Even for smaller processes there are many potential constraints. Moreover, there may be many constraints that are trivially true and that do not characterize the process well. Naively checking all possible constraints is computationally intractable and may lead to models with an excessive number of constraints. Therefore, we have developed an Apriori algorithm to reduce the search space. Moreover, we use new metrics to prune the model. As a result, we can quickly generate understandable Declare models for real-life event logs

Mining inter-organizational business process models from EDI messages : a case study from the automotive sector

Traditional standards for Electronic Data Interchange (EDI), such as EDIFACT and ANSI X12, have been employed in Business-to-Business (B2B) e-commerce for decades. Due to their wide industry coverage and long-standing establishment, they will presumably continue to play an important role for some time. EDI systems are typically not "process-aware", i.e., messages are standardized but processes simply "emerge". However, to improve performance and to enhance the control, it is important to understand and analyze the "real" processes supported by these systems. In the case study presented in this paper we uncover the inter-organizational business processes of an automotive supplier company by analyzing the EDIFACT messages that it receives from its business partners. We start by transforming a set of observed messages to an event log, which requires that the individual messages are correlated to process instances. Thereby, we make use of the specific structure of EDIFACT messages. Then we apply process mining techniques to uncover the inter-organizational business processes. Our results show that inter-organizational business process models can be derived by analyzing EDI messages that are exchanged in a network of organizations