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

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

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.

Extending XQuery with pattern matching over XML, HTML and JSON, and its usage for data mining

2014 ◽  
Author(s):  
Benito van der Zander
Keyword(s):  
Data Mining ◽  
Programming Languages ◽  
Pattern Matching ◽  
Large Scale ◽  
General Pattern ◽  
Real Thing ◽  
Functional Programming Languages ◽  
Large Scale Data ◽  
Definition Of ◽  
Scale Data

Pattern matching in a broad sense is a common feature of modern functional programming languages, answering the question, if one complex structured object has a form that is the same as another complex structured object, for some definition of “the same”. In XQuery path expressions, switch, and typeswitch statements are often described as performing pattern matching, but these are merely impoverished flavors of matching when compared to the real thing. We describe a syntax for general pattern matching based on regular expressions for XML/HTML/JSONiq trees, how these patterns are matched against input data, and how this pattern matching can be integrated into the syntax and semantics of the XQuery language. At the end we summarize real-world experience using it for large-scale data mining of library webcatalogs.

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