Top-k Algorithm Based on Extraction
Algorithms for top-k query are widely used in massive data query, which return k most important objects based on aggregate functions. The classical Threshold Algorithm (TA) is one of the most famous algorithms for top-k query. It requires sequential and random accesses to the lists. The time cost of TA will be very high when data is massive. This paper proposes a new algorithm TABE (Top-k Algorithm Based on Extraction) to minimize the query time. TABE first extracts the objects which have higher ranking on each attribute, and then execute the Threshold Algorithm on these objects. Test results show that TABE has high accuracy to meet the general query requirements, and the experimental results of comparing TABE with NRA (No Random Accesses) show that our proposed algorithm TABE can largely reduce the query time.
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