Category-Based Infidelity Bounded Queries over Unstructured Data Streams

2013 ◽  
Vol 25 (11) ◽  
pp. 2448-2462 ◽  
Author(s):  
Manish Bhide ◽  
Krithi Ramamritham

2021 ◽  
Author(s):  
Piyush Yadav ◽  
Dhaval Salwala ◽  
Bharath Sudharsan ◽  
Edward Curry


Author(s):  
Daniel Gerber ◽  
Sebastian Hellmann ◽  
Lorenz Bühmann ◽  
Tommaso Soru ◽  
Ricardo Usbeck ◽  
...  


2019 ◽  
Vol 6 (22) ◽  
pp. 159355
Author(s):  
Najam Sahar ◽  
Muhammad Irshad ◽  
Muhammad Khan




Author(s):  
LAKSHMI PRANEETHA

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.



2017 ◽  
Author(s):  
Roshni Kalbhore ◽  
Pravin Malviya




2014 ◽  
Author(s):  
Wenkuang Wu ◽  
Xiaoguang Lu ◽  
Ben Cox ◽  
Guoqiang Li ◽  
Lihua Lin ◽  
...  


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