Hybrid knowledge fusion and inference on cloud environment

In the age of big data, an incredible amount of knowledge is produced everywhere everyday through various ways. Knowledge fusion and inference thus has become an important issue for better utilization of knowledge. In this paper, we focus on the hybrid knowledge that can be represented in the form of association rules (with categorical and/or numerical information), and address the problem of such hybrid knowledge fusion and inference on cloud environment. In light of the use of the problem, we specify six issues of knowledge fusion and inference and propose a HyKFICE (Hybrid Knowledge Fusion and Inference on Cloud Environment) system as an effective solution. HyKFICE is capable of inferring the possibilities of the presence of items at a given condition through knowledge fusion and inference based on the probability theory. HyKFICE can also perform the computation in parallel on clouds by grouping and summing up similar knowledge in 3-layer bipartite graphs. Experiments conducted on real data sets demonstrate the efficiency of HyKFICE and show that it is not only the amount of knowledge but also the associations between knowledge dominating the execution time of the hybrid knowledge fusion and inference.