Metadata Management for Textual Documents in Data Lakes

TL;DR

This paper presents a specialized metadata management approach for textual documents in data lakes, addressing the gap in handling unstructured data to prevent data swamp issues.

Contribution

It introduces a methodological framework for extracting, storing, and reusing metadata specific to textual documents in data lakes, validated through the COREL project.

Findings

Identified key metadata types for textual documents

Developed techniques for metadata extraction from text

Validated approach within the COREL project

Abstract

Data lakes have emerged as an alternative to data warehouses for the storage, exploration and analysis of big data. In a data lake, data are stored in a raw state and bear no explicit schema. Thence, an efficient metadata system is essential to avoid the data lake turning to a so-called data swamp. Existing works about managing data lake metadata mostly focus on structured and semi-structured data, with little research on unstructured data. Thus, we propose in this paper a methodological approach to build and manage a metadata system that is specific to textual documents in data lakes. First, we make an inventory of usual and meaningful metadata to extract. Then, we apply some specific techniques from the text mining and information retrieval domains to extract, store and reuse these metadata within the COREL research project, in order to validate our proposals.