Continuous monitoring of skylines over uncertain data streams

Ding, Xiuli; Lian, Xiang; Chen, Lei; Jin, Hai

doi:10.1016/j.ins.2011.09.007

Cited by 53 publications

(36 citation statements)

References 23 publications

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“…However, the uncertainty is inevitable in many applications due to various factors such as the limitations of measuring equipment and delays in data updates [22]. For example, the data collected from wireless sensors will be affected by many random factors such as the change of temperature, the change of air humidity and the communication barriers.…”

Section: Related Workmentioning

confidence: 99%

A Tree-based Concept Drift Detection Method by Three-way Decisions

Su¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. Concept drift detection is an active research area in data stream mining. The existing concept drift detection methods usually determine a concept is drifted or not drifted. In other words, the concepts are assigned into two classes such as drifted and un-drifted, which is typically based on two-way decisions. Most likely, these methods incorrectly determine that concept drift occurs due to some uncertain factors such as noise, and some real concept drifts are not detected. Inspired by the three-way decision theory, we propose a tree-based concept drift detection method by three-way decisions in this paper, in order to improve the accuracy of detection. The basic idea of the method is to assign the concepts into three classes such as drifted, nondeterministic drifted and un-drifted. Furthermore, concepts in the class of nondeterministic drifted are determined further according to the deviation between the classification error rates. The results of comparison experiments show the effectiveness and efficiency of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

A Tree-based Concept Drift Detection Method by Three-way Decisions

Su¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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“…The authors in [8] investigated skyline probabilities with a parametric form pdf (e.g., a Gaussian function or a Gaussian mixture model). The authors in [22] proposed a sliding window skyline model to study the execution of the probabilistic skyline query over uncertain data streams [23]. The authors studied a new problem of range-based skyline queries.…”

Section: Related Workmentioning

confidence: 99%

Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network

Pan

Dong

Cao

et al. 2014

International Journal of Distributed Sensor Networks

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In moving environment, the positions of moving objects cannot be located accurately. Apart from the measuring instrument errors, movement of the objects is the main factor contributing to this uncertainty. This uncertainty makes dominant relationship of data instable, which will affect skyline operator. In this paper, we mainly study the continuous probabilistic skyline query for uncertain moving objects in road network. The query point is deemed to be stationary while moving objects are treated as targets with uncertainty described by a probability density function. After defining the notion of dominant probability and probabilistic skyline, we put forward a novel algorithm to deal with continuous probabilistic skyline query on road network. Firstly, we compute the dominant probability and skyline probability to get initial permanent p-skyline set. Then we define events to predict the time when dominant relationship between moving objects may change. Furthermore, we track and calculate events to update the probabilistic skyline in an incremental way. Two pruning strategies are proposed to cancel invalid events and objects in a bid to diminish search space. Finally, an extensive experimental evaluation on real datasets shows that probabilistic skyline sets in road network can be updated by the proposed algorithm. It demonstrates both efficiency and effectiveness.

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“…Namely, stab the intervals by M−n+1, and then return the data elements e such that each κ(e) is the right end of a stabbed interval. Figure 1, the dominant graph can be encoded by the following intervals: (0,3], (0,4], (3,7], (4,5], and (4,6]. When n=6, M−n+1=2 as M=7.…”

Section: Preliminariesmentioning

confidence: 99%

“…). So the interval tree I R N ;q consists the following intervals by encoding the dominance relations in the dominance graph G R N ;q : (0,2], (0,3] and (2,6]. If n=5 (to retrieve probabilistic skylines within the recent 6 elements), we stab the interval tree I R N ;q with M−n+ 1 = 3 and c,g will be returned as the final results.…”

Section: Minimizing the Number Of Uncertain Elements And The Encodingmentioning

confidence: 99%

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Probabilistic n-of-N skyline computation over uncertain data streams

Zhang

Cheema

et al. 2014

World Wide Web

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Skyline operator is a useful tool in multi-criteria decision making in various applications. Uncertainty is inherent in real applications due to various reasons. In this paper, we consider the problem of efficiently computing probabilistic skylines against the most recent N uncertain elements in a data stream seen so far. Specifically, we study the problem in the nof-N model; that is, computing the probabilistic skyline for the most recent n (∀n≤N) elements, where an element is a probabilistic skyline element if its skyline probability is not below a given probability threshold q. Firstly, an effective pruning technique to minimize the number of uncertain elements to be kept is developed. It can be shown that on average storing only O(log d N) uncertain elements from the most recent N elements is sufficient to support the precise computation of all probabilistic n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. A novel encoding scheme is then proposed together with efficient update techniques so that computing a probabilistic n-of-N skyline query in a d-dimension space is reduced to O(dloglogN+s) if the data distribution is independent, where s is the number of skyline points. A trigger based technique is provided to process continuous n-of-N skyline queries. Extensive experiments demonstrate that the new techniques World Wide Web on uncertain data streams can support on-line probabilistic skyline query computation over rapid data streams.

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Continuous monitoring of skylines over uncertain data streams

Cited by 53 publications

References 23 publications

A Tree-based Concept Drift Detection Method by Three-way Decisions

A Tree-based Concept Drift Detection Method by Three-way Decisions

Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network

Probabilistic n-of-N skyline computation over uncertain data streams

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