Enabling Personalized Process Schedules with Time-Aware Process Views

Abstract

Summary. Companies increasingly adopt process-aware information systems (PAISs) to model, enact, monitor, and evolve their business processes. Although the proper handling of temporal constraints (e.g., deadlines and minimum time lags between activities) is crucial for many application domains, existing PAISs vary significantly regarding the support of the temporal perspective of a business process. In previous work, we introduced characteristic time patterns for specifying the temporal perspective of PAISs. However, time-aware process schemas might be complex and hard to understand for end-users. To enable their proper visualization, therefore, this paper introduces an approach for transforming time-aware process schemas into enhanced Gantt charts. Based on this, a method for creating personalized process schedules using process views is suggested. Overall, the presented approach enables users to easily understand and monitor time-aware processes in PAISs.

The Repercussions of Business Process Modeling Notations on Mental Load and Mental Effort

“…eGantt [19] eGantt Chart is an extension of the Gantt Chart with an emphasis to capture time characteristics of a process in a process model. EPC [5] EPC is a flow chart type mainly used for the implementation and configuration of enterprise resource planning.…”

Enabling a User-Friendly Visualization of Business Process Models

“…All these approaches focus on technical issues, whereas issues related to the graphical representation of process artifacts (e.g., process tasks or data objects) are factored out. Various visualization concepts are provided in [21], which focus on visualizing traffic in process models, whereas [22] presents visualization concepts for time-aware process models. Finally, niPRO enables advanced interactions based on a sophisticated navigation concept [16,23,24].…”

Pruning Based Service Selection Approach Under QoS and Temporal Constraints

“…A solution of the optimization problem is then a set of the largest possible time intervals of all tasks. For instance, given the example presented in Figure 1, the largest time slots of all tasks when considering all imposed constraints and with a deadline equals to 23 are respectively: [8,16], [10,20], [11,20] and [14,23]. Based on these intervals, some instances have to be pruned (e.g., S 112 and S 131 ) or some restrictions have to be performed to their intervals (e.g., S 222 ).…”

“…Based on these models, authors proposed heuristic algorithms to achieve better performance. In [8], Canfora et al model and resolve the service selection problem based on genetic algorithms. These approaches, however, do not provide strategies to reduce the search space before selection and do not cater for temporal properties.…”