Processing Massive Data Streams to Achieve Anomaly Intrusion Prevention

2012 ◽  
Author(s):  
C. Kavitha ◽  
M. Suresh
Keyword(s):  
Data Streams ◽  
Massive Data ◽  
Intrusion Prevention

scholarly journals Tryton Supercomputer Capabilities for Analysis of Massive Data Streams

2015 ◽  
Vol 22 (3) ◽  
pp. 99-104 ◽  
Author(s):  
Henryk Krawczyk ◽  
Michał Nykiel ◽  
Jerzy Proficz
Keyword(s):  
Data Streams ◽  
3D Visualization ◽  
Massive Data ◽  
Software Components ◽  
Network Nodes ◽  
Computer Center ◽  
University Of Technology ◽  
The Status ◽  
Visualization Techniques ◽  
Massive Parallel Processing

Abstract The recently deployed supercomputer Tryton, located in the Academic Computer Center of Gdansk University of Technology, provides great means for massive parallel processing. Moreover, the status of the Center as one of the main network nodes in the PIONIER network enables the fast and reliable transfer of data produced by miscellaneous devices scattered in the area of the whole country. The typical examples of such data are streams containing radio-telescope and satellite observations. Their analysis, especially with real-time constraints, can be challenging and requires the usage of dedicated software components. We propose a solution for such parallel analysis using the supercomputer, supervised by the KASKADA platform, which with the conjunction with immerse 3D visualization techniques can be used to solve problems such as pulsar detection and chronometric or oil-spill simulation on the sea surface.

scholarly journals Real Time Anomaly Detection in Massive Data Streams with ELK Stack

2019 ◽  
Vol 15 (6) ◽  
pp. 814-823
Author(s):  
Jakup Fondaj ◽  
Zirije Hasani
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Data Streams ◽  
Massive Data

scholarly journals An Approximate L1 -Difference Algorithm for Massive Data Streams

2002 ◽  
Vol 32 (1) ◽  
pp. 131-151 ◽  
Author(s):  
Joan Feigenbaum ◽  
Sampath Kannan ◽  
Martin J. Strauss ◽  
Mahesh Viswanathan
Keyword(s):  
Data Streams ◽  
Massive Data

Knowledge Discovery From Massive Data Streams

2018 ◽  
pp. 1508-1534
Author(s):  
Sushil Kumar Narang ◽  
Sushil Kumar ◽  
Vishal Verma
Keyword(s):  
Knowledge Discovery ◽  
Data Streams ◽  
Coming Out ◽  
Explicit Knowledge ◽  
Massive Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Comprehensive Knowledge ◽  
Tools And Techniques ◽  
Constructing Knowledge

T.S. Eliot once wrote some beautiful poetic lines including one “Where is the knowledge we have lost in information?”. Can't say that T.S. Eliot could have anticipated today's scenario which is emerging from his poetic lines. Data in present scenario is a profuse resource in many circumstances and is piling-up and many technical leaders are finding themselves drowning in data. Through this big stream of data there is a vast flood of information coming out and seemingly crossing manageable boundaries. As Information is a necessary channel for educing and constructing knowledge, one can assume the importance of generating new and comprehensive knowledge discovery tools and techniques for digging this overflowing sea of information to create explicit knowledge. This chapter describes traditional as well as modern research techniques towards knowledge discovery from massive data streams. These techniques have been effectively applied not exclusively to completely structured but also to semi-structured and unstructured data. At the same time Semantic Web technologies in today's perspective require many of them to deal with all sorts of raw data.

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