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Effective Monotone Knowledge Integration in Kernel Support Vector Machines Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Scalable Document-Based Architecture for Text Analysis

Authors : Ciprian-Octavian Truică, Jérôme Darmont, Julien Velcin

Published in: Advanced Data Mining and Applications

Publisher: Springer International Publishing

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Abstract

Analyzing textual data is a very challenging task because of the huge volume of data generated daily. Fundamental issues in text analysis include the lack of structure in document datasets, the need for various preprocessing steps and performance and scaling issues. Existing text analysis architectures partly solve these issues, providing restrictive data schemas, addressing only one aspect of text preprocessing and focusing on one single task when dealing with performance optimization. Thus, we propose in this paper a new generic text analysis architecture, where document structure is flexible, many preprocessing techniques are integrated and textual datasets are indexed for efficient access. We implement our conceptual architecture using both a relational and a document-oriented database. Our experiments demonstrate the feasibility of our approach and the superiority of the document-oriented logical and physical implementation.

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Footnotes
2
Collected with Twitter’s tools at https://​dev.​twitter.​com.
 
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Metadata
Title
A Scalable Document-Based Architecture for Text Analysis
Authors
Ciprian-Octavian Truică
Jérôme Darmont
Julien Velcin
Copyright Year
2016
Book
Advanced Data Mining and Applications

Print ISBN: 978-3-319-49585-9

Electronic ISBN: 978-3-319-49586-6

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-49586-6_33

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