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Your search keyword '"Li, Fei"' showing total 2 results
Start Over Author "Li, Fei" Topic computer science
2 results on '"Li, Fei"'

1. A comprehensive study of an online packet scheduling algorithm.

Author

Li, Fei

Subjects
*DATA packeting, *COMPUTER algorithms, *COMPUTER scheduling, *DISCRETE systems, *BUFFER storage (Computer science), *COMPUTER science
Abstract

Abstract: We study the bounded-delay model for Qualify-of-Service buffer management. Time is discrete. There is a buffer. Unit-length jobs (also called packets) arrive at the buffer over time. Each packet has an integer release time, an integer deadline, and a positive real value. A packet’s characteristics are not known to an online algorithm until the packet actually arrives. In each time step, at most one packet can be sent out of the buffer. The objective is to maximize the total value of the packets sent by their respective deadlines in an online manner. An online algorithm’s performance is usually measured in terms of competitive ratio, when this online algorithm is compared with a clairvoyant algorithm achieving the maximum total value. In this paper, we study a simple and intuitive online algorithm. We analyze its performance in terms of competitive ratio for the general model and a few important variants. [Copyright &y& Elsevier]

Published
2013
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2. Approximate joins for XML at label level.

Author

Li, Fei, Wang, Hongzhi, Hao, Liang, Li, Jianzhong, and Gao, Hong

Subjects
*BIG data, *PROBABILITY theory, *SCALABILITY, *SYSTEMS design, *COMPUTER science, *XML (Extensible Markup Language)
Abstract

In heterogeneous XML data sources, the same real-world object may not be represented exactly the same. Thus approximate join techniques are often applied, in which XML documents are joined based on similarity. In previous XML join methods, researchers consider each XML label as a unit and entirely disregard the similarity between different labels. However, real-world data sets are often 'dirty'. The labels should be also approximately matched in the join. To improve the join quality, our approach considers both XML structure and node label similarity by applying two tailored similarity measures. Min-hash, a probabilistic hash function, is employed to achieve scalability. Extensive experiments confirm that the join quality is fundamentally improved when the label similarity is considered and our join efficiency is even higher than some of the most efficient methods. [ABSTRACT FROM AUTHOR]

Published
2014
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