Running multi-relational data mining processes in the cloud: A practical approach for social networks

Abstract

Multi-relational Data Mining algorithms (MRDM) are the appropriate approach for inferring knowledge from databases containing multiple relationships between non-homogenous entities, which are precisely the case of datasets obtained from social networks. However, to acquire such expressivity, the search space of candidate hypotheses in MRDM algorithms is more complex than those obtained from traditional data mining algorithms. To allow a feasible search space of hypotheses, MRDM algorithms adopt several language biases during the mining process. Because of that, when running a MRDM-based system, the user needs to execute the same set of data mining tasks a number of times, each assuming a different combination of parameters in order to get a final good hypothesis. This makes manual control of such complex process tedious, laborious and error-prone. In addition, running the same MRDM process several times consumes much time. Thus, the automatic execution of each setting of parameters throughout parallelization techniques becomes essential. In this paper, we propose an approach named LPFlow4SN that models a MRDM process as a scientific workflow and then executes it in parallel in the cloud, thus benefiting from the existing Scientific Workflow Management Systems. Experimental results reinforce the potential of running parallel scientific workflows in the cloud to automatically control the MRDM process while improving its overall execution performance.