A Scalable Bloom Filter for Membership Queries

A Load Balancing Mechanism Using Bloom Filter in Storm System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1972 ◽

2014 ◽

Vol 543-547 ◽

pp. 1972-1976

Author(s):

Huai Lin Dong ◽

Ming Yuan He ◽

Qing Feng Wu ◽

Sheng Hang Wu

Keyword(s):

Data Structure ◽

Load Balancing ◽

Real Time ◽

Data Transmission ◽

Streaming Media ◽

Bloom Filter ◽

Time System ◽

Real Time System ◽

Membership Queries ◽

Probabilistic Data Structure

When membership queries are evaluated in a set, the performance can be improved by a Bloom filter which is a space-efficient probabilistic data structure. According to its space-efficient character, Bloom Filter presented to address the load balancing problem for streaming media information in Storm system which is free and open source distributed real time computation system. This method increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves real time system Storm efficiently in saving the data transmission time and reducing the calculation complexity.

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Importance-aware Bloom Filter for managing set membership queries on streaming data

2013 Fifth International Conference on Communication Systems and Networks (COMSNETS) ◽

10.1109/comsnets.2013.6465564 ◽

2013 ◽

Cited By ~ 4

Author(s):

Ravi Bhoraskar ◽

Vijay Gabale ◽

Purushottam Kulkarni ◽

Dhananjay Kulkarni

Keyword(s):

Bloom Filter ◽

Streaming Data ◽

Membership Queries ◽

Set Membership

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Magic Cube Bloom Filter: Answering Membership Queries for Multiple Sets

2019 IEEE International Conference on Big Data and Smart Computing (BigComp) ◽

10.1109/bigcomp.2019.8679119 ◽

2019 ◽

Author(s):

Zhouyi Sun ◽

Siang Gao ◽

Bingqing Liu ◽

Yufei Wang ◽

Tong Yang ◽

...

Keyword(s):

Bloom Filter ◽

Membership Queries ◽

Magic Cube

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Constant-Weight Group Coded Bloom Filter for Multiple-Set Membership Queries

Advances in Intelligent Systems and Computing - The 10th International Conference on Computer Engineering and Networks ◽

10.1007/978-981-15-8462-6_83 ◽

2020 ◽

pp. 723-730

Author(s):

Xiaomei Tian ◽

Huihuang Zhao

Keyword(s):

Bloom Filter ◽

Weight Group ◽

Constant Weight ◽

Membership Queries ◽

Set Membership

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FBF: Bloom Filter for Fuzzy Membership Queries on Strings

2021 IEEE 15th International Conference on Semantic Computing (ICSC) ◽

10.1109/icsc50631.2021.00010 ◽

2021 ◽

Author(s):

Rishabh Kumar ◽

Hari Prasanna P ◽

Mukund Rungta ◽

Swetha Kashinath Phuleker ◽

Hemant Tiwari ◽

...

Keyword(s):

Bloom Filter ◽

Fuzzy Membership ◽

Membership Queries

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

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i10.400 ◽

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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