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Exploiting Formal Concept Analysis for Data Modeling in Data Lakes

Anes Bendimerad, Romain Mathonat, Youcef Remil, Mehdi Kaytoue

TL;DR

This work addresses the challenge of exploiting heterogeneous data-lake contents by applying Formal Concept Analysis (FCA) to unify data models. Data structures such as InfluxDB measurements and Elasticsearch indexes are represented as objects and their fields as attributes, from which a concept lattice is derived to guide unification. The authors develop two strategies, top-down and bottom-up, to merge disparate field names into a common schema, achieving a 54% reduction in distinct field names (190 down to 88) and covering up to 75–80% of data structures with a compact set of field names (as low as 25–34 names in different contexts). The resulting unified schema supports more accessible data modeling and can inform data-exchange components (e.g., Copilote EDI), with future work proposed in NLP and graph mining to scale to larger lakes and automate mappings.

Abstract

Data lakes are widely used to store extensive and heterogeneous datasets for advanced analytics. However, the unstructured nature of data in these repositories introduces complexities in exploiting them and extracting meaningful insights. This motivates the need of exploring efficient approaches for consolidating data lakes and deriving a common and unified schema. This paper introduces a practical data visualization and analysis approach rooted in Formal Concept Analysis (FCA) to systematically clean, organize, and design data structures within a data lake. We explore diverse data structures stored in our data lake at Infologic, including InfluxDB measurements and Elasticsearch indexes, aiming to derive conventions for a more accessible data model. Leveraging FCA, we represent data structures as objects, analyze the concept lattice, and present two strategies-top-down and bottom-up-to unify these structures and establish a common schema. Our methodology yields significant results, enabling the identification of common concepts in the data structures, such as resources along with their underlying shared fields (timestamp, type, usedRatio, etc.). Moreover, the number of distinct data structure field names is reduced by 54 percent (from 190 to 88) in the studied subset of our data lake. We achieve a complete coverage of 80 percent of data structures with only 34 distinct field names, a significant improvement from the initial 121 field names that were needed to reach such coverage. The paper provides insights into the Infologic ecosystem, problem formulation, exploration strategies, and presents both qualitative and quantitative results.

Exploiting Formal Concept Analysis for Data Modeling in Data Lakes

TL;DR

This work addresses the challenge of exploiting heterogeneous data-lake contents by applying Formal Concept Analysis (FCA) to unify data models. Data structures such as InfluxDB measurements and Elasticsearch indexes are represented as objects and their fields as attributes, from which a concept lattice is derived to guide unification. The authors develop two strategies, top-down and bottom-up, to merge disparate field names into a common schema, achieving a 54% reduction in distinct field names (190 down to 88) and covering up to 75–80% of data structures with a compact set of field names (as low as 25–34 names in different contexts). The resulting unified schema supports more accessible data modeling and can inform data-exchange components (e.g., Copilote EDI), with future work proposed in NLP and graph mining to scale to larger lakes and automate mappings.

Abstract

Data lakes are widely used to store extensive and heterogeneous datasets for advanced analytics. However, the unstructured nature of data in these repositories introduces complexities in exploiting them and extracting meaningful insights. This motivates the need of exploring efficient approaches for consolidating data lakes and deriving a common and unified schema. This paper introduces a practical data visualization and analysis approach rooted in Formal Concept Analysis (FCA) to systematically clean, organize, and design data structures within a data lake. We explore diverse data structures stored in our data lake at Infologic, including InfluxDB measurements and Elasticsearch indexes, aiming to derive conventions for a more accessible data model. Leveraging FCA, we represent data structures as objects, analyze the concept lattice, and present two strategies-top-down and bottom-up-to unify these structures and establish a common schema. Our methodology yields significant results, enabling the identification of common concepts in the data structures, such as resources along with their underlying shared fields (timestamp, type, usedRatio, etc.). Moreover, the number of distinct data structure field names is reduced by 54 percent (from 190 to 88) in the studied subset of our data lake. We achieve a complete coverage of 80 percent of data structures with only 34 distinct field names, a significant improvement from the initial 121 field names that were needed to reach such coverage. The paper provides insights into the Infologic ecosystem, problem formulation, exploration strategies, and presents both qualitative and quantitative results.
Paper Structure (20 sections, 1 theorem, 13 figures, 1 table)

This paper contains 20 sections, 1 theorem, 13 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Infologic
  4. Data lake
  5. Problem Formulation
  6. Data model with FCA
  7. Concept lattice
  8. Exploiting the Concept Lattice
  9. Analyzing the initial lattice
  10. The top-down approach
  11. Unifying generic fields from InfluxDB and Elasticsearch.
  12. Unifying the $user$ field.
  13. The bottom-up approach
  14. Unifying the $code$ attribute.
  15. Unifying the $type$ attribute.
  16. ...and 5 more sections

Key Result

theorem thmcountertheorem

The pair of functions $(ext, int)$ form a Galois connection between the power set lattices $(2^{\mathcal{G}}, \subseteq)$ and $(2^{\mathcal{M}}, \subseteq)$. That is, $ext \circ int$ and $int \circ ext$ are closure operators on $(2^{\mathcal{G}}, \subseteq)$ and, $(2^{\mathcal{M}}, \subseteq)$ respe

Figures (13)

  • Figure 1: Simplified architecture of predictive maintenance at Infologic DBLP:conf/sigsoft/BendimeradRMK23.
  • Figure 2: Data structures stored in our data lake and studied in this paper.
  • Figure 3: The concept lattice before and after unifying fields from Table \ref{['tab:formalContext']}.
  • Figure 4: Concept lattice depicting data structures within our data lake. The objects (data structures) are indicated in the lattice.
  • Figure 5: Concept lattice depicting data structures within our data lake. The attributes (field names) are indicated in the lattice.
  • ...and 8 more figures

Theorems & Definitions (1)

  • theorem thmcountertheorem