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

ADSTREAM: Anomaly Detection in Large-Scale Data Streams Using Local Outlier Factor Based on Micro-Cluster

2017 ◽  
Vol 23 (10) ◽  
pp. 10204-10209 ◽  
Author(s):  
Sanghyun Seo ◽  
Seongchul Park ◽  
Injea Hwang ◽  
Juntae Kim
Keyword(s):  
Anomaly Detection ◽  
Data Streams ◽  
Large Scale ◽  
Local Outlier Factor ◽  
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.

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