An approach to online Bayesian learning from multiple data streams

We present a collective approach to mine Bayesian networks from distributed heterogenous web-log data streams. In this approach we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, that models the entire data. This technique is then suitably adapted to an online Bayesian learning technique, where the network parameters are updated sequentially based on new data from multiple streams. We applied this technique to mine multiple data streams where data centralization is difficult because of large response time and scalability issues. This approach is particularly suitable for mining applications with distributed sources of data streams in an environment with non-zero communication cost (e.g. wireless networks). Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.