A novel consensus-based ranking method, named BC ranking, is proposed for the problem of top k query on multivalued objects. The Effective and efficient algorithms are developed to compute the top k query based on BC ranks. The Effective pruning techniques are proposed to significantly improve the performance in terms of CPU and I/O costs. A cost model is proposed to analyze the I/O costs of the algorithms. Experiments demonstrate that our cost model is highly accurate. We present case studies on real data sets to demonstrate the effectiveness of the generalized BC ranking. We also use synthetic and real data sets to verify the efficiency of our computational method.
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