Mining Massive Hierarchical Data Using a Scalable Probabilistic
  Graphical Model
  Probabilistic Graphical Models (PGM) are very useful in the fields of machine
learning and data mining. The crucial limitation of those models,however, is
the scalability. The Bayesian Network, which is one of the most common PGMs
used in machine learning and data mining, demonstrates this limitation when the
training data consists of random variables, each of them has a large set of
possible values. In the big data era, one would expect new extensions to the
existing PGMs to handle the massive amount of data produced these days by
computers, sensors and other electronic devices. With hierarchical data - data
that is arranged in a treelike structure with several levels - one would expect
to see hundreds of thousands or millions of values distributed over even just a
small number of levels. When modeling this kind of hierarchical data across
large data sets, Bayesian Networks become infeasible for representing the
probability distributions. In this paper we introduce an extension to Bayesian
Networks to handle massive sets of hierarchical data in a reasonable amount of
time and space. The proposed model achieves perfect precision of 1.0 and high
recall of 0.93 when it is used as multi-label classifier for the annotation of
mass spectrometry data. On another data set of 1.5 billion search logs provided
by CareerBuilder.com the model was able to predict latent semantic
relationships between search keywords with accuracy up to 0.80.
