April 5, 2010
DASFAA, an annual international database conference which showcases state-of-the-art R & D activities in database systems and their applications has selected a paper submited by the department of Computer Science for Best Paper Award.
The paper is entitled “Detecting Leaders from Correlated Time Series”,
by Di Wu, Yiping Ke, Jeffrey Xu Yu, Philip S. Yu, Lei Chen
A brief description of the paper:
Nowadays, the WorldWideWeb is full of rich information, including text data, XML data, multimedia data, time series data, etc. The web is usually represented as a large graph and PageRank is computed to rank the importance of web pages. This paper studies the problem of ranking evolving time series and discovering leaders from them by analyzing lead-lag relations. A time series is considered to be one of the leaders if its rise or fall impacts the behavior of many other time series. At each time point, we compute the lagged correlation between each pair of time series and model them in a graph. Then, the leadership rank is computed from the graph, which brings order to time series. Based on the leadership ranking, the leaders of time series are extracted. However, the problem poses great challenges since the dynamic nature of time series results in a highly evolving graph, in which the relationships between time series are modeled. The paper proposes an efficient algorithm which is able to track the lagged correlation and compute the leaders incrementally, while still achieving good accuracy. The experiments on real weather science data and stock data show that our algorithm is able to compute time series leaders efficiently in a real-time manner and the detected leaders demonstrate high predictive power on the event of general time series entities, which can enlighten both weather monitoring and financial risk control.
The conference accepted 55 full papers out of around 250 submissions.