A Visual Interactive Analytics Interface for Complex Event Processing and Machine Learning Processing of Financial Market Data

Applying machine learning techniques over streaming data is notoriously difficult as it involves the interplay of several technologies that need to be judiciously put together by IT experts. This has motivated the need to provide intuitive and easy to use interactive interfaces for financial experts...

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Published in:Proceedings / International Conference on Information Visualisation pp. 189 - 194
Main Authors: Tri Luong, Nhan Nathan, Milosevic, Zoran, Berry, Andrew, Rabhi, Fethi
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2020
Subjects:
artificial intelligence
complex event processing
Computer architecture
Data visualization
directional changes
Engines
financial market data
Machine learning
real-time analytics
streaming
Training
visual analytics
Visualization
ISSN:2375-0138
Online Access:Get full text
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Abstract Applying machine learning techniques over streaming data is notoriously difficult as it involves the interplay of several technologies that need to be judiciously put together by IT experts. This has motivated the need to provide intuitive and easy to use interactive interfaces for financial experts to be able to leverage both Complex Event Processing (CEP) and Machine Learning (ML) technologies. The solution proposed in this paper is based on an open architecture that uses CEP engines as a pre-processing function for downstream ML training and prediction computations. We demonstrate this approach using a few scenarios involving the analysis of financial market data streams.
AbstractList Applying machine learning techniques over streaming data is notoriously difficult as it involves the interplay of several technologies that need to be judiciously put together by IT experts. This has motivated the need to provide intuitive and easy to use interactive interfaces for financial experts to be able to leverage both Complex Event Processing (CEP) and Machine Learning (ML) technologies. The solution proposed in this paper is based on an open architecture that uses CEP engines as a pre-processing function for downstream ML training and prediction computations. We demonstrate this approach using a few scenarios involving the analysis of financial market data streams.
Author Berry, Andrew
Tri Luong, Nhan Nathan
Milosevic, Zoran
Rabhi, Fethi
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  email: f.rabhi@unsw.edu.au
  organization: University of New South Wales,School of Computer Science and Engineering,Sydney,Australia
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Snippet Applying machine learning techniques over streaming data is notoriously difficult as it involves the interplay of several technologies that need to be...
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StartPage 189
SubjectTerms artificial intelligence
complex event processing
Computer architecture
Data visualization
directional changes
Engines
financial market data
Machine learning
real-time analytics
streaming
Training
visual analytics
Visualization
Title A Visual Interactive Analytics Interface for Complex Event Processing and Machine Learning Processing of Financial Market Data
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