.png){kind=icon}
.png){kind=icon}
Return
Efficient Top-k Dominating Computation on Massive Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2017.2665619.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In many applications, top-k dominating query is an important operation to return k tuples with the highest domination scores in a potentially huge data space. It is analyzed that the existing algorithms have their performance problems when performed on massive data. This paper proposes a novel table-scan-based TDTS algorithm to efficiently compute top-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 number of tuples maintained in TDTS can be reduced substantially. The extensive experimental results, conducted on synthetic and real-life data sets, show that TDTS outperforms the existing algorithms significantly.
Keywords:
Massive data
TDTS algorithm
table scan
early termination
pruning operation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.7K
Citations:
3.2W