Social software for modeling business processes

The aim of this paper is to show how the use of social networks may help users to behave as modelers they trust. Users are guided in this respect within the context of an existing Recommendation-Based Process Modeling Support System to which social features are added. Two kinds of social networks are used to this end: (1) a social network from a process model repository and (2) a social network from a recommendation history. The social network from process models provides an organizational view of business processes. An example of the information that could be derived from such a network is the average distance between performers who belong to part of business process that is already modeled and the ones who belong to a candidate process. A user can apply this result to complete a process model in a way that is similar to earlier selected solutions. The social network from recommendation history shows the relationship among modelers who use the recommendation system. From its usage history, social networks can be generated that express the similarity between its nodes (users). Both approaches are presented as effective ways to exploit social relationships in capturing business processes in conceptual models, one of the key activities in the BPM domain

Volterra-based nonlinear compensation in 400 Gb/s WDM multiband coherent optical OFDM systems

\u3cp\u3eWe apply a 3\u3csup\u3erd\u3c/sup\u3e-order inverse Volterra series nonlinear equalizer to a 400 Gb/s WDM multiband PM-16QAM OFDM signal. IVSTF-NLE provides a 0.6 dB Q-factor improvement and 1 dB nonlinear threshold increase compared to linear equalization.\u3c/p\u3

Discovering colored Petri nets from event logs

Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models