A MapReduce architecture for web site user behaviour monitoring in real time

Monitoring the behaviour of large numbers of web site users in real time poses significant performance challenges, due to the decentralised location and volume of generated data. This paper proposes a MapReduce-style architecture where the processing of event series from the Web users is performed by a number of cascading mappers, reducers and rereducers, local to the event origin. With the use of static analysis and a prototype implementation, we show how this architecture is capable to carry out time series analysis in real time for very large web data sets, based on the actual events, instead of resorting to sampling or other extrapolation techniques

Dynamic human resource selection for business process exceptions

A key capability of today's organizations is to flexibly and effectively react to unexpected events. A critical case of an unexpected event is sudden unavailability of human resources, which was not properly addressed by existing resource allocation approaches. This paper proposes a systematic approach that analyzes event logs to select suitable substitutes if the initial human resources become unavailable. The approach uses process mining and social network analysis to derive a metric called degree of substitution, which measures how much the work experiences of the human resources overlap, from the two perspectives: task execution and transfer of work. Along with the metric, suitable substitutes are also identified. A simulation demonstrates that the approach identifies suitable substitutes more effectively and accurately than existing allocation methods such as role‐based allocation or random allocation. The proposed approach will increase the effectiveness of dynamic allocation of human resources, especially in an exceptional situation.11Yscopu