KIDS:K-anonymization data stream base on sliding window

2010 ◽  
Author(s):  
Junwei Zhang ◽  
Jing Yang ◽  
Jianpei Zhang ◽  
Yongbin Yuan
Keyword(s):  
Data Stream ◽  
Sliding Window

Mining top-k high-utility itemsets from a data stream under sliding window model

2017 ◽  
Vol 47 (4) ◽  
pp. 1240-1255 ◽  
Author(s):  
Siddharth Dawar ◽  
Veronica Sharma ◽  
Vikram Goyal
Keyword(s):  
Data Stream ◽  
Sliding Window ◽  
High Utility ◽  
High Utility Itemsets

Estimation of the Number of Distinct Values over Data Stream Based on Compound Sliding Window

2013 ◽  
Vol 8 (1) ◽  
Author(s):  
Yingli Zhong ◽  
Jinghua Zhu ◽  
Meirui Ren ◽  
Yan Yang
Keyword(s):  
Data Stream ◽  
Sliding Window

scholarly journals A Method for Processing Top-k Continuous Query on Uncertain Data Stream in Sliding Window Model

2021 ◽  
Vol 16 ◽  
pp. 261-269
Author(s):  
Raja Azhan Syah Raja Wahab ◽  
Siti Nurulain Mohd Rum ◽  
Hamidah Ibrahim ◽  
Fatimah Sidi ◽  
Iskandar Ishak
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Data Stream ◽  
Uncertain Data ◽  
Research Work ◽  
Computational Cost ◽  
Sliding Window ◽  
Possible World ◽  
Processing Methods ◽  
Uncertain Data Streams

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.

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