EMD-DSJoin: Efficient Similarity Join Over Probabilistic Data Streams Based on Earth Mover’s Distance

2016 ◽  
pp. 42-54 ◽  
Author(s):  
Jia Xu ◽  
Jiazhen Zhang ◽  
Chao Song ◽  
Qianzhen Zhang ◽  
Pin Lv ◽  
...  
Keyword(s):  
Data Streams ◽  
Earth Mover’S Distance ◽  
Probabilistic Data ◽  
Similarity Join ◽  
Earth Mover's Distance ◽  
Probabilistic Data Streams

Efficient and effective similarity search over probabilistic data based on Earth Mover’s Distance

2011 ◽  
Vol 21 (4) ◽  
pp. 535-559 ◽  
Author(s):  
Jia Xu ◽  
Zhenjie Zhang ◽  
Anthony K. H. Tung ◽  
Ge Yu
Keyword(s):  
Similarity Search ◽  
Earth Mover’S Distance ◽  
Probabilistic Data ◽  
Earth Mover's Distance

scholarly journals Outlier Detection over Sliding Windows for Probabilistic Data Streams

2010 ◽  
Vol 25 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Bin Wang ◽  
Xiao-Chun Yang ◽  
Guo-Ren Wang ◽  
Ge Yu
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Probabilistic Data ◽  
Sliding Windows ◽  
Probabilistic Data Streams

PROBABILISTIC QUERYING OVER UNCERTAIN DATA STREAMS

2012 ◽  
Vol 20 (05) ◽  
pp. 701-728 ◽  
Author(s):  
MOHAMMAD G. DEZFULI ◽  
MOSTAFA S. HAGHJOO
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Memory Management ◽  
Query Language ◽  
Uncertain Data ◽  
Sensor Data ◽  
Data Stream Management ◽  
Probabilistic Data ◽  
Stream Management ◽  
Probabilistic Data Streams

Inherent imprecision of data in many applications motivates us to support uncertainty as a first-class concept. Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor data management systems and object monitoring systems which need both issues in tandem. Our main contribution is designing a probabilistic data stream management system, called Sarcheshmeh, for continuous querying over probabilistic data streams. Sarcheshmeh supports uncertainty from input data to final query results. In this paper, after reviewing requirements and applications of probabilistic data streams, we present our new data model for probabilistic data streams and define our main logical operators formally. Then, we present query language and physical operators. In addition, we introduce the architecture of Sarcheshmeh and also describe some major challenges like memory management and our floating precision mechanism toward designing a more robust system. Finally, we report evaluation of our system and the effect of floating precision on the tradeoff between accuracy and efficiency.

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