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Probabilistic Graphical Models (PGM) are very useful in the fields of machinelearning and data mining. The crucial limitation of those models,however, isthe scalability. The Bayesian Network, which is one of the most common PGMsused in machine learning and data mining, demonstrates this limitation when thetraining data consists of random variables, each of them has a large set ofpossible values. In the big data era, one would expect new extensions to theexisting PGMs to handle the massive amount of data produced these days bycomputers, sensors and other electronic devices. With hierarchical data - datathat is arranged in a treelike structure with several levels - one would expectto see hundreds of thousands or millions of values distributed over even just asmall number of levels. When modeling this kind of hierarchical data acrosslarge data sets, Bayesian Networks become infeasible for representing theprobability distributions. In this paper we introduce an extension to BayesianNetworks to handle massive sets of hierarchical data in a reasonable amount oftime and space. The proposed model achieves perfect precision of 1.0 and highrecall of 0.93 when it is used as multi-label classifier for the annotation ofmass spectrometry data. On another data set of 1.5 billion search logs providedby CareerBuilder.com the model was able to predict latent semanticrelationships between search keywords with accuracy up to 0.80.

Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model