Mining Multi-source Data to Study Workplace Activity Patterns

Abstract

Examining work activity patterns is a problem of enduring research in organizations. The fortuitous availability of a whole new set of data collection mechanisms such as mobiles, activity loggers, GPS based location detectors, provide us new ways of studying workplace behaviour. We present a data collection framework that helps in collection, anonymization, fusion, processing and mining of behavioural data. We use the framework to study the activities in a research and development team with an aim to find the relationship between behavioural traits, states, and activity patterns. We find partial support for the claim that behavioral states and activity patterns are associated.