DocumentCode :
44029
Title :
A Generic Framework for Top- {schmi k} Pairs and Top- {schmi k} Objects Queries over Sliding
Author :
Zhitao Shen ; Cheema, Muhammad Aamir ; Xuemin Lin ; Wenjie Zhang ; Haixun Wang
Author_Institution :
Sch. of Comput. Sci. & Eng., Univ. of New South Wales, Kensington, NSW, Australia
Volume :
26
Issue :
6
fYear :
2014
fDate :
Jun-14
Firstpage :
1349
Lastpage :
1366
Abstract :
Top-k pairs and top-k objects queries have received significant attention by the research community. In this paper, we present the first approach to answer a broad class of top-k pairs and top-k objects queries over sliding windows. Our framework handles multiple top-k queries and each query is allowed to use a different scoring function, a different value of k, and a different size of the sliding window. Furthermore, the framework allows the users to define arbitrarily complex scoring functions and supports out-of-order data streams. For all the queries that use the same scoring function, we need to maintain only one K-skyband. We present efficient techniques for the K-skyband maintenance and query answering. We conduct a detailed complexity analysis and show that the expected cost of our approach is reasonably close to the lower bound cost. For top-k pairs queries, we demonstrate the efficiency of our approach by comparing it with a specially designed supreme algorithm that assumes the existence of an oracle and meets the lower bound cost. For top-k objects queries, our experimental results demonstrate the superiority of our algorithm over the state-of-the-art algorithm.
Keywords :
query processing; K-skyband maintenance; arbitrarily complex scoring functions; out-of-order data streams; query answering; sliding windows; top-k objects queries; top-k pairs; Algorithm design and analysis; Complexity theory; Data engineering; Database systems; Educational institutions; Knowledge engineering; Maintenance engineering; Top-$k$ pairs; data streams; sliding windows; top-$k$ objects; top-$k$ queries;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2012.181
Filename :
6305452
Link To Document :
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