Information technology (IT) is increasingly playing a crucial role in managing business processes across all industries and organizations but business managers and analysts seem to underestimate the impact of usability of IT solutions on processes and people. This paper focuses on the usability of one of the most popular business process management software systems, SAP, which comes as an integrated solution that incorporates the key business functions and processes of an organization. The paper critically analyzes related issues and implications using the System Usability Scale (SUS) and semi-structured interviews. Several considerations and suggestions are drawn in terms of rethinking and pursuing usability in training when applied to Enterprise Resource Planning (ERP) and other systems supporting business process management.

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Abstract-Adoption of Cloud Computing is on the rise [1] and many datacenter operators adhere to strict energy efficiency guidelines [2]. In this paper a novel approach to scheduling in a Cloud Computing context is proposed. The algorithm Maxmin Fast Track (MXFT) revises the Max-min algorithm to better support smaller tasks with stricter Service Level Agreements (SLAs), which makes it more relevant to Cloud Computing. MXFT is inspired by queuing in supermarkets, where there is a fast lane for customers with a smaller number of items. The algorithm outperforms Max-min in task execution times and outperforms Min-min in overall makespan. A by-product of investigating this algorithm was the development of simulator called "ScheduleSim" [3] which makes it simpler to prove a scheduling algorithm before committing to a specific scheduling problem in Cloud Computing and therefore might be a useful precursor to experiments using the established simulator CloudSim [4].

Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.