NDLI: Loyalty-based selection: retrieving objects that persistently satisfy criteria

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Loyalty-based selection: retrieving objects that persistently satisfy criteria

Content Provider	ACM Digital Library
Author	Shen, Zhitao Lin, Xuemin Cheema, Muhammad Aamir
Abstract	A traditional query returns a set of objects that satisfy user defined criteria at the time query was issued. The results are based on the values of objects at query time and may be affected by outliers. Intuitively, an object better meets the user's needs if it persistently satisfies the criteria, i.e., it satisfies the criteria for majority of the time in the past T time units. In this paper, we propose a measure named loyalty that reflects how persistently an object satisfies the criteria. Formally, the loyalty of an object is the total time (in past T time units) it satisfies the query criteria. In this paper, we study top-k loyalty queries over sliding windows that continuously report k objects with the highest loyalties. Each object issues an update when it starts satisfying the criteria or when it stops satisfying the criteria. We show that the lower bound cost of updating the results of a top-k loyalty query is O(logN), for each object update, where N is the number of updates issued in last T time units. We conduct a detailed complexity analysis and show that our proposed algorithm is optimal. Moreover, effective pruning techniques are proposed to improve the efficiency. We experimentally verify the effectiveness of the proposed approach by comparing it with a classic sweep line algorithm.
Starting Page	2189
Ending Page	2193
Page Count	5
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398599
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Loyalty queries Temporal data Data streams
Content Type	Text
Resource Type	Article

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