A Scalable Document-based Architecture for Text Analysis

TL;DR

This paper introduces a scalable, flexible document-based architecture for text analysis that integrates multiple preprocessing techniques and improves performance through efficient indexing, demonstrated with relational and document-oriented databases.

Contribution

A novel generic text analysis architecture combining flexible document structure, multiple preprocessing steps, and efficient indexing, implemented with relational and document-oriented databases.

Findings

Document-oriented implementation outperforms relational databases in scalability.

Flexible architecture supports various preprocessing techniques.

Feasibility demonstrated through experimental evaluation.

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 %(e.g., stem or lemma extraction, part-of-speech tagging, named entities recognition...), 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. %As a result, no definite solution is currently available. 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.