There have been many researches and semantics in answering top-k queries on uncertain data in various applications. However, most of these semantics must consume much of their time in computing position probability. O...There have been many researches and semantics in answering top-k queries on uncertain data in various applications. However, most of these semantics must consume much of their time in computing position probability. Our approach to support various top-k queries is based on position probability distribution （PPD） sharing. In this paper, a PPD-tree structure and several basic operations on it are proposed to support various top-k queries. In addition, we proposed an approximation method to improve the efficiency of PPD generation. We also verify the effectiveness and efficiency of our approach by both theoretical analysis and experiments.展开更多
Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. ...Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection （CUOD）, which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach -- Probability Pruning for Continuous Uncertain Outlier Detection （PCUOD） to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.展开更多
Data uncertainty widely exists in many web applications, financial applications and sensor networks. Ranking queries that return a number of tuples with maximal ranking scores are important in the field of database ma...Data uncertainty widely exists in many web applications, financial applications and sensor networks. Ranking queries that return a number of tuples with maximal ranking scores are important in the field of database management. Most existing work focuses on proposing static solutions for various ranking semantics over uncertain data. Our focus is to handle continuous ranking queries on uncertain data streams： testing each new tuple to output highly-ranked tuples. The main challenge comes from not only the fact that the possible world space will grow exponentially when new tuples arrive, but also the requirement for low space- and time- complexity to adapt to the streaming environments. This paper aims at handling continuous ranking queries on uncertain data streams. We first study how to handle this issue exactly, then we propose a novel method （exponential sampling） to estimate the expected rank of a tuple with high quality. Analysis in theory and detailed experimental reports evaluate the proposed methods.展开更多
不确定数据流上的Skyline查询技术逐步引起研究者的关注,传统的集中式流处理算法难以满足海量数据的查询需求,并且云计算所提供的海量计算资源和有效的存储管理模式,为研究并行Skyline查询技术提供了充足的条件。基于上述事实,提出了一...不确定数据流上的Skyline查询技术逐步引起研究者的关注,传统的集中式流处理算法难以满足海量数据的查询需求,并且云计算所提供的海量计算资源和有效的存储管理模式,为研究并行Skyline查询技术提供了充足的条件。基于上述事实,提出了一种不确定数据流上的并行Skyline查询算法(parallel Skyline over uncertain data streams,PSUDS)。该算法通过交叉划分滑动窗口的方式,将集中式流查询转化为并行处理,以并行执行的方式来解决集中式算法处理性能不足的问题。大量实验结果表明,该算法具有较好的并行可扩展性。展开更多
基金Supported by the National High Technology Research and Development Program of China(863 Program 2012AA011004)the National Natural Science Foundation of China(61232002,61202033)Natural Science Foundation of Hubei Province(2011CDB448)
文摘There have been many researches and semantics in answering top-k queries on uncertain data in various applications. However, most of these semantics must consume much of their time in computing position probability. Our approach to support various top-k queries is based on position probability distribution （PPD） sharing. In this paper, a PPD-tree structure and several basic operations on it are proposed to support various top-k queries. In addition, we proposed an approximation method to improve the efficiency of PPD generation. We also verify the effectiveness and efficiency of our approach by both theoretical analysis and experiments.
基金supported by the National Natural Science Foundation of China under Grant Nos.61025007,61328202,61173029,61100024,61332006,and 61073063the National High Technology Research and Development 863 Program of China under Grant No.2012AA011004the National Basic Research 973 Program of China under Grant No.2011CB302200-G
文摘Outlier detection on data streams is an important task in data mining. The challenges become even larger when considering uncertain data. This paper studies the problem of outlier detection on uncertain data streams. We propose Continuous Uncertain Outlier Detection （CUOD）, which can quickly determine the nature of the uncertain elements by pruning to improve the efficiency. Furthermore, we propose a pruning approach -- Probability Pruning for Continuous Uncertain Outlier Detection （PCUOD） to reduce the detection cost. It is an estimated outlier probability method which can effectively reduce the amount of calculations. The cost of PCUOD incremental algorithm can satisfy the demand of uncertain data streams. Finally, a new method for parameter variable queries to CUOD is proposed, enabling the concurrent execution of different queries. To the best of our knowledge, this paper is the first work to perform outlier detection on uncertain data streams which can handle parameter variable queries simultaneously. Our methods are verified using both real data and synthetic data. The results show that they are able to reduce the required storage and running time.
文摘Data uncertainty widely exists in many web applications, financial applications and sensor networks. Ranking queries that return a number of tuples with maximal ranking scores are important in the field of database management. Most existing work focuses on proposing static solutions for various ranking semantics over uncertain data. Our focus is to handle continuous ranking queries on uncertain data streams： testing each new tuple to output highly-ranked tuples. The main challenge comes from not only the fact that the possible world space will grow exponentially when new tuples arrive, but also the requirement for low space- and time- complexity to adapt to the streaming environments. This paper aims at handling continuous ranking queries on uncertain data streams. We first study how to handle this issue exactly, then we propose a novel method （exponential sampling） to estimate the expected rank of a tuple with high quality. Analysis in theory and detailed experimental reports evaluate the proposed methods.
文摘不确定数据流上的Skyline查询技术逐步引起研究者的关注,传统的集中式流处理算法难以满足海量数据的查询需求,并且云计算所提供的海量计算资源和有效的存储管理模式,为研究并行Skyline查询技术提供了充足的条件。基于上述事实,提出了一种不确定数据流上的并行Skyline查询算法(parallel Skyline over uncertain data streams,PSUDS)。该算法通过交叉划分滑动窗口的方式,将集中式流查询转化为并行处理,以并行执行的方式来解决集中式算法处理性能不足的问题。大量实验结果表明,该算法具有较好的并行可扩展性。
