Authors:
Pegdwendé N. Sawadogo
1
;
Tokio Kibata
2
and
Jérôme Darmont
1
Affiliations:
1
Université de Lyon, Lyon 2, ERIC EA 3083, 5 avenue Pierre Mendès France, F69676, Bron and France
;
2
Université de Lyon, Ecole Centrale de Lyon, 36 avenue Guy de Collongue, F69134, Ecully and France
Keyword(s):
Data Lakes, Textual Documents, Metadata Management, Data Ponds.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Coupling and Integrating Heterogeneous Data Sources
;
Data Mining
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Non-Relational Databases
;
Sensor Networks
;
Signal Processing
;
Soft Computing
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.