Citation: Karakostas, B. and Theodoulidis, B. (2013). A MapReduce architecture for web site user behaviour monitoring in real time. Paper presented at the 2nd International Conference on Data Management Technologies and Applications (DATA), 29 -31 July 2013, Reykjavik, Iceland. This is the unspecified version of the paper. This version of the publication may differ from the final published version. 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. Permanent
Karakostas, Bill and others survey research on the MapReduce architecture for web site user behaviour monitoring in real time REF , in this paper, a MapReduce style architecture is proposed, where the processing of event series from the web users is performed by a number of cascading mappers, reducers, local to the event origin, the experimental results show that 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.
A MapReduce architecture for web site user behaviour monitoring in real time
DATA
Computer Science