Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index

2014 ◽  
Vol 41 (2) ◽  
pp. 277-309 ◽  
Author(s):  
Xiaoyong Li ◽  
Yijie Wang ◽  
Xiaoling Li ◽  
Yuan Wang
2021 ◽  
Vol 16 ◽  
pp. 261-269
Author(s):  
Raja Azhan Syah Raja Wahab ◽  
Siti Nurulain Mohd Rum ◽  
Hamidah Ibrahim ◽  
Fatimah Sidi ◽  
Iskandar Ishak

The data stream is a series of data generated at sequential time from different sources. Processing such data is very important in many contemporary applications such as sensor networks, RFID technology, mobile computing and many more. The huge amount data generated and frequent changes in a short time makes the conventional processing methods insufficient. The Sliding Window Model (SWM) was introduced by Datar et. al to handle this problem. Avoiding multiple scans of the whole data sets, optimizing memory usage, and processing only the most recent tuple are the main challenges. The number of possible world instances grows exponentially in uncertain data and it is highly difficult to comprehend what it takes to meet Top-k query processing in the shortest amount of time. Following the generation of rules and the probability theory of this model, a framework was anticipated to sustain top-k processing algorithm over the SWM approach until the candidates expired. Based on the literature review study, none of the existing work have been made to tackle the issue arises from the top-k query processing of the possible world instance of the uncertain data streams within the SWM. The major issue resulted from these scenarios need to be addressed especially in the computation redundancy area that contributed to the increases of computational cost within the SWM. Therefore, the main objective of this research work is to propose the top-k query processing methods over uncertain data streams in SWM utilizing the score and the Possible World (PW) setting. In this study, a novel expiration and object indexing method is introduced to address the computational redundancy issues. We believed the proposed method can reduce computational costs and by managing insertion and exit policy on the right tuple candidates within a specified window frame. This research work will contribute to the area of computational query processing.


2013 ◽  
Vol 380-384 ◽  
pp. 2681-2686
Author(s):  
Yong Tao Yang ◽  
Yi Jie Wang ◽  
Min Guo ◽  
Xiao Yong Li

Reverse skyline is useful for supporting many applications, such as marketing decision,environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a needfor processing probabilistic reverse skyline queries. In this paper, we study the problem of efficientlyprocessing these queries on uncertain data streams. We first show the formal definitions of reverseskyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRSto maintain the most recent N uncertain data elements and to process continuous queries on them.CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structurenamed Characteristic Rectangle, are incorporated into it to handling the extra computing complexityarising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniquesare very efficient in handling uncertain data streams.


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