Straggler Identification in Round-Trip Data Streams via Newton's Identities and Invertible Bloom Filters

D Eppstein; M T Goodrich

doi:10.1109/tkde.2010.132

Straggler Identification in Round-Trip Data Streams via Newton's Identities and Invertible Bloom Filters

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2010.132 ◽

2011 ◽

Vol 23 (2) ◽

pp. 297-306 ◽

Cited By ~ 27

Author(s):

D Eppstein ◽

M T Goodrich

Keyword(s):

Data Streams ◽

Bloom Filters ◽

Round Trip ◽

Newton’S Identities

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Space-Efficient Straggler Identification in Round-Trip Data Streams Via Newton’s Identities and Invertible Bloom Filters

Lecture Notes in Computer Science - Algorithms and Data Structures ◽

10.1007/978-3-540-73951-7_55 ◽

2007 ◽

pp. 637-648 ◽

Cited By ~ 6

Author(s):

David Eppstein ◽

Michael T. Goodrich

Keyword(s):

Data Streams ◽

Bloom Filters ◽

Round Trip ◽

Newton’S Identities

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Time-Decaying Bloom Filters for Data Streams with Skewed Distributions

15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05) ◽

10.1109/ride.2005.15 ◽

2005 ◽

Cited By ~ 20

Author(s):

Kai Cheng ◽

Limin Xiang ◽

M. Iwaihara ◽

Haiyan Xu ◽

M.M. Mohania

Keyword(s):

Data Streams ◽

Bloom Filters ◽

Skewed Distributions

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Advanced Algorithms for Efcient Approximate Duplicate Detection in Data Streams Using Bloom Filters

Large Scale and Big Data ◽

10.1201/b17112-17 ◽

2014 ◽

pp. 424-449

Keyword(s):

Data Streams ◽

Bloom Filters ◽

Duplicate Detection

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Using Bloom Filters for Mining Top-k Frequent Itemsets in Data Streams

Communications in Computer and Information Science - Secure and Trust Computing, Data Management and Applications ◽

10.1007/978-3-642-22339-6_25 ◽

2011 ◽

pp. 209-216

Author(s):

Younghee Kim ◽

Kyungsoo Cho ◽

Jaeyeol Yoon ◽

Ieejoon Kim ◽

Ungmo Kim

Keyword(s):

Data Streams ◽

Frequent Itemsets ◽

Bloom Filters

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Stable learned bloom filters for data streams

Proceedings of the VLDB Endowment ◽

10.14778/3407790.3407830 ◽

2020 ◽

Vol 13 (12) ◽

pp. 2355-2367

Author(s):

Qiyu Liu ◽

Libin Zheng ◽

Yanyan Shen ◽

Lei Chen

Keyword(s):

Data Streams ◽

Bloom Filters

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Round Trip Through German Education

Contemporary Psychology ◽

10.1037/006179 ◽

1960 ◽

Vol 5 (10) ◽

pp. 326-327

Author(s):

ERNST G. BEIER

Keyword(s):

Round Trip ◽

German Education

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Online Feature Extraction Algorithms for Data Streams

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.132.6 ◽

2012 ◽

Vol 132 (1) ◽

pp. 6-13

Author(s):

Seiichi Ozawa

Keyword(s):

Feature Extraction ◽

Data Streams

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An Online Framework for Flow Round Trip Time Measurement

IEICE Transactions on Communications ◽

10.1587/transcom.e97.b.2145 ◽

2014 ◽

Vol E97.B (10) ◽

pp. 2145-2156

Author(s):

Xinjie GUAN ◽

Xili WAN ◽

Ryoichi KAWAHARA ◽

Hiroshi SAITO

Keyword(s):

Time Measurement ◽

Round Trip Time ◽

Round Trip ◽

Trip Time ◽

Flow Round

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Filtering of Mixed Data Streams with Orthogonal Polarization up to 50 Gbps in Micro-Ring/Bus Waveguide

2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) ◽

10.23919/ps.2019.8817775 ◽

2019 ◽

Author(s):

Zih-Chun Su ◽

Chih-Hsien Cheng ◽

Bo-Ji Huang ◽

Huai-Yung Wang ◽

Chun-Nien Liu ◽

...

Keyword(s):

Data Streams ◽

Mixed Data ◽

Orthogonal Polarization

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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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