scholarly journals Adaptive sampling for geometric problems over data streams

2008 ◽  
Vol 39 (3) ◽  
pp. 191-208 ◽  
Author(s):  
John Hershberger ◽  
Subhash Suri
Keyword(s):  
Data Streams ◽  
Adaptive Sampling ◽  
Geometric Problems

A spatial-adaptive sampling procedure for online monitoring of big data streams

2018 ◽  
Vol 50 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Andi Wang ◽  
Xiaochen Xian ◽  
Fugee Tsung ◽  
Kaibo Liu
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Adaptive Sampling ◽  
Online Monitoring ◽  
Sampling Procedure ◽  
Big Data Streams

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

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.

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