article · 2024
K-Regret Query Algorithm over Streaming Data
Cited by 0Abstract and details only
Abstract
Selecting a portion of points that users are interested in many applications such as online search and recommendation when facing large amounts of data. The k-regret problem was recently proposed for the selection of high-quality objects. However, current research methods for k-regret queries are designed for static data environments and cannot effectively maintain query results in high-speed streaming data. In order to support this query, we proposes a query processing framework called KPSW (K-regret Partition Sliding Window) based on sliding windows. Firstly, KPSW utilizes the characteristics of data flow to partition windows. Secondly, it predicts the earliest time when new points entering the window become the predicted results, facilitate real-time provision of the latest results in high-speed flow environments. Finally, experimental results on several real and synthetic datasets show that KPSW runs faster than existing static algorithms, while providing almost identical quality result.
Cite this paper
An, Y., Chen, K., & Zhu, R. (2024). K-Regret Query Algorithm over Streaming Data. https://doi.org/10.1109/ngdn61651.2024.10744089
An, Y., et al. “K-Regret Query Algorithm over Streaming Data.” 2024, pp. 380–83, https://doi.org/10.1109/ngdn61651.2024.10744089.
An, Y., Kai Chen, and Rui Zhu. 2024. “K-Regret Query Algorithm over Streaming Data.” https://doi.org/10.1109/ngdn61651.2024.10744089.
An, Y., Chen, K. and Zhu, R. (2024) ‘K-Regret Query Algorithm over Streaming Data’. Available at: https://doi.org/10.1109/ngdn61651.2024.10744089.
@article{an2024regret,
title = {{K-Regret Query Algorithm over Streaming Data}},
author = {An, Y. and Chen, Kai and Zhu, Rui},
pages = {380--383},
year = {2024},
doi = {10.1109/ngdn61651.2024.10744089}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.