scholarly journals Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams

2021 ◽  
Author(s):  
Mahardhika Pratama ◽  
Choiru Za’in ◽  
Edwin Lughofer ◽  
Eric Pardede ◽  
Dwi A.P. Rahayu
Keyword(s):  
Data Streams ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

Scalable KDE-based top-n local outlier detection over large-scale data streams

2020 ◽  
Vol 204 ◽  
pp. 106186 ◽  
Author(s):  
Fang Liu ◽  
Yanwei Yu ◽  
Peng Song ◽  
Yangyang Fan ◽  
Xiangrong Tong
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data ◽  
Local Outlier

scholarly journals CVS: Fast cardinality estimation for large-scale data streams over sliding windows

2016 ◽  
Vol 194 ◽  
pp. 107-116 ◽  
Author(s):  
Jingsong Shan ◽  
Jianxin Luo ◽  
Guiqiang Ni ◽  
Zhaofeng Wu ◽  
Weiwei Duan
Keyword(s):  
Data Streams ◽  
Large Scale ◽  
Sliding Windows ◽  
Cardinality Estimation ◽  
Large Scale Data ◽  
Scale Data

scholarly journals An online classification algorithm for large scale data streams: iGNGSVM

2017 ◽  
Vol 262 ◽  
pp. 67-76 ◽  
Author(s):  
Andrés L. Suárez-Cetrulo ◽  
Alejandro Cervantes
Keyword(s):  
Data Streams ◽  
Large Scale ◽  
Classification Algorithm ◽  
Large Scale Data ◽  
Online Classification ◽  
Scale Data

Management of Data Streams for Large-Scale Data Mining

2011 ◽  
pp. 136-153
Author(s):  
Jon R. Wright ◽  
Gregg T. Vesonder ◽  
Tamraparni Dasu
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Information Acquisition ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Mining Operation ◽  
Large Scale Data ◽  
Technical Issues ◽  
Automate Information ◽  
Scale Data

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

Dynamic pattern matching with multiple queries on large scale data streams

2020 ◽  
Vol 171 ◽  
pp. 107402
Author(s):  
S. Sukhanov ◽  
R. Wu ◽  
C. Debes ◽  
A.M. Zoubir
Keyword(s):  
Pattern Matching ◽  
Data Streams ◽  
Large Scale ◽  
Dynamic Pattern ◽  
Multiple Queries ◽  
Large Scale Data ◽  
Scale Data
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