Time-Decaying Bloom Filters for Data Streams with Skewed Distributions

2005 ◽  
Author(s):  
Kai Cheng ◽  
Limin Xiang ◽  
M. Iwaihara ◽  
Haiyan Xu ◽  
M.M. Mohania
Keyword(s):  
Data Streams ◽  
Bloom Filters ◽  
Skewed Distributions

Mining Data Streams with Skewed Distribution based on Ensemble Method

2012 ◽  
Vol 4 (4) ◽  
pp. 52-62
Author(s):  
Yi Wang
Keyword(s):  
Predictive Modeling ◽  
Data Streams ◽  
Data Stream ◽  
Skewed Distribution ◽  
Ensemble Method ◽  
Skewed Data ◽  
Skewed Distributions ◽  
Mining Data Streams ◽  
Sample Method ◽  
Effective Result

In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but cannot handle well skewed (e.g., few positives but lots of negatives) and skewed distributions, which are typical in many data stream applications. In this paper, we propose an ensemble and cluster based sample method to deal with this situation. The study shows that this method has effective result on skewed data streams mining.

Stable learned bloom filters for data streams

2020 ◽  
Vol 13 (12) ◽  
pp. 2355-2367
Author(s):  
Qiyu Liu ◽  
Libin Zheng ◽  
Yanyan Shen ◽  
Lei Chen
Keyword(s):  
Data Streams ◽  
Bloom Filters

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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