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PROJECT TITLE :

Efficient Top-k Dominating Computation on Massive Data - 2017

ABSTRACT:

In many applications, prime-k dominating query is a crucial operation to come back k tuples with the highest domination scores in a doubtless huge information area. It is analyzed that the present algorithms have their performance issues when performed on large information. This paper proposes a completely unique table-scan-primarily based TDTS algorithm to efficiently compute high-k dominating results. TDTS first presorts the table for early termination. The early termination checking is proposed in this paper, along with the theoretical analysis of scan depth. The pruning operation for tuples is devised in this paper. The theoretical pruning effect shows that the amount of tuples maintained in TDTS will be reduced substantially. The intensive experimental results, conducted on artificial and real-life knowledge sets, show that TDTS outperforms the existing algorithms considerably.


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