Mashroom+: An Interactive Data Mashup Approach with Uncertainty Handling

Abstract

To integrate data on the Internet, we often have to deal with uncertainties when matching data schemas from different sources. The paper proposes an approach called Mashroom+ to support human-machine interactive data mashup, which can better handle uncertainties during the semantic matching process. To improve the correctness of matching results, an interactive matching algorithm is proposed to synthesize the matching results from multiple automatic matchers based on user feedbacks. Meanwhile, to avoid bringing too much burden on users, we utilize the entropy in information theory to measure and quantify the ambiguities of different matchers and calculate the best times for users to participate. An interactive integration environment is developed based on our approach with operator recommendation capability to support on-demand data integration. Experiments show that Mashroom+ approach can achieve good balance between high correctness of matching results and low user burden with real data.