Data Preprocessing for Cross-System Analysis: The DaProXSA Approach

Abstract

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work.