Flexibility and adaptability have been praised and empirically validated as critical for an organization's performance. They are associated with the number of different behaviors that the organization exhibits, as well as with their level of sophistication, which in turn implies the number and the quality of the available knowledge and capabilities in place. In this work, we propose the creation of a bipartite network, involving organizations and behaviors, through an event log, to capture the prospects of both organizations and behaviors for process innovation. By using the Method of Reflections, we claim that the structure of that network can expose a set of sophistication metrics, that eventually suggest i) the potential of behaviors for better organizational performance; ii) the most reachable innovation paths; iii) an "order of significance" over the behaviors considering their improvement contribution power. We show the practical potential of our approach through a Proof of Concept based on a real dataset.

Descriptive data analysis is used for mining spatial and temporal patterns from ski lift entrance data. The data, collected through radio-frequency identification scanners, cover one skiing season with approximately 1.2 million recorded ski lift transportations. Cluster analysis was performed on ten subsamples, and the obtained clusters were cross-validated. Several types of skier behavior were found. Temporal clustering revealed that skier patterns differ according to time of maximal performance and length of stay in the ski lift transportation system. Spatial clustering revealed that it is reasonable to have as many clusters as ski lifts in a ski resort, since skiers tend to choose a dominant ski lift during a skier-day. The detected patterns reveal valuable information that can be used for potential improvement of products and services offered by ski resorts.

Resource allocation and scheduling are fundamental issues in a Workflow Management System (WfMS). Effective resource management in WfMS should examine resource allocation together with task scheduling since these problems impose mutual constraints. Optimization of the one factor is subject to the other constraints and vice versa. Thus, an ideal algorithm should take into account not only performance metrics of the infrastructure, such as the number of resources and their utilization, but also quality criteria such as the percentage of tasks undergone violation in their temporal restrictions. In this paper, we propose an innovative algorithm which jointly optimizes the two aforementioned contradictory criteria. The algorithm, called Resource Conflicts Joint Optimization (Re.Co.Jo.Op.), minimizes resource conflicts subject to temporal constraints and simultaneously optimizes throughput or utilization subject to resources constraints. To achieve the optimization, the two factors are formulated in a matrix form and the optimal solution is found by applying concepts of the generalized eigenvalue analysis. A rough outline of an agent-based architecture is proposed to achieve runtime integration of our algorithm into a functional WfMS, while experimental results under different load environments and tasks assumption reveal the superiority of the proposed strategy than the other conventional approaches.

The optimal product line design problem consists of predicting the acceptance of new products in the market, prior of the actual production phase. It requires a unique blend of techniques and resources as it deals with multiple parameters and hard optimization problems. This paper proposes a service oriented architecture which outlines the basic features that a marketing decision support system should provide in order to address this problem. Each feature corresponds to a distinct service while the overall functionality is achieved though service orchestration. The proposed breakdown of the problem into distinct services fits intrinsically the way that marketing managers anticipate the optimal product line process, while it allows them the opportunity to get a solution without getting obstructed of the various, complex optimization parameters and needing to coordinate the whole process.