Name
Abstract | ||
|---|---|---|
Continuous top-k query over sliding window is a fundamental problem in the domain of streaming data management, which monitors the query window and retrieves k objects with the highest scores when the window slides. The key of supporting this query is maintaining a subset of objects in the window, and try to retrieve answers from them when the window slides. The state-of-the-art approach called SAP utilizes the partition technique to support top-k searches. Its key idea is using, as few as possible, high-quality candidates to support the query via finding a proper partition. However, it has to waste relatively high computation cost in evaluating whether the partition is proper and re-scanning the widow. In this paper, we propose an ELM-based framework named EPF, which improves SAP via learning the nature of streaming data. If we learn that the distribution of streaming data is predictable, we could construct a suitable prediction model for a more efficient partition of the window. Furthermore, we propose a novel algorithm to reduce the re-scanning cost. We conduct a thorough experimental study of this technique on real and synthetic datasets and show the significant performance improvement when applying the technique in existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s12559-019-09661-z | Cognitive Computation |
Keywords | Field | DocType |
ELM stream classification top-k
| Data mining,Sliding window protocol,Computer science,Artificial intelligence,Streaming data,Partition (number theory),Machine learning,Computation,Performance improvement | Journal |
Volume | Issue | ISSN |
12 | 1 | 1866-9956 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Jiang | 1 | 8 | 9.37 |
| Rui Zhu | 2 | 21 | 3.96 |
| Bin Wang | 3 | 0 | 0.34 |