A Process for Inference of Columnar NoSQL Database Schemas

Frozza, Angelo Augusto; Defreyn, Eduardo Dias; Mello, Ronaldo dos Santos

doi:10.5753/sbbd.2020.13637

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…This argument could be extended further: Even basic data preprocessing and delivery of first insights is still tedious with tabular data in conjunction with today's tooling. Data engineering libraries such as Pandas and Spark provide only minimal schema inference capabilities on a columnar level, and NoSQL databases such as HBase require even manual inference [4]. Tools like SDV are highly capable, but also dependency-heavy and focused on model training rather than fast detailed schema inference.…”

Section: Motivationmentioning

confidence: 99%

Tabular Data Insights and Synthesis with the AutoTable Approach

Spillner

2022

2022 9th Swiss Conference on Data Science (SDS)

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AI convergence platforms such as Google's Unified AI Platform promise to fully interpret and understand any data submitted to them. The business needs of SMEs are however better addressed by tailored tools that smartly parse and interpret data without being locked into a particular vendor platform. With AutoTable, a new tool design for schema, pattern and relation inference as well as training data synthesis has recently become available. This paper explains why AutoTable is smart, unintrusive and yet powerful in working with tabular business data such as CSVs, flat JSON and spreadsheets.

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Section: Motivationmentioning

confidence: 99%

Tabular Data Insights and Synthesis with the AutoTable Approach

Spillner

2022

2022 9th Swiss Conference on Data Science (SDS)

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A universal approach for multi-model schema inference

2022

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The variety feature of Big Data, represented by multi-model data, has brought a new dimension of complexity to all aspects of data management. The need to process a set of distinct but interlinked data models is a challenging task. In this paper, we focus on the problem of inference of a schema, i.e., the description of the structure of data. While several verified approaches exist in the single-model world, their application for multi-model data is not straightforward. We introduce an approach that ensures inference of a common schema of multi-model data capturing their specifics. It can infer local integrity constraints as well as intra- and inter-model references. Following the standard features of Big Data, it can cope with overlapping models, i.e., data redundancy, and it is designed to process efficiently significant amounts of data.To the best of our knowledge, ours is the first approach addressing schema inference in the world of multi-model databases.

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Schema Extraction in NoSQL Databases: A Systematic Literature Review

Belefqih,

Zellou,

Berquedich

2024

RACSC

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Introduction: Nowadays, NoSQL databases have taken on an increasingly important role in the storage of massive data within companies. Due to a common property called schema-less, NoSQL databases offer great flexibility, particularly for the storage of data in different formats. However, despite their success in data storage, schema-less databases are a major obstacle in areas requiring precise knowledge of this schema, especially in the field of data integration. Method: This study presents a Systematic Literature Review (SLR) to explore, evaluate, and discuss relevant existing research and endeavors using novel schema extraction approaches. Furthermore, we conducted this study using a well-defined methodology to examine and study the problem of schema extraction from NoSQL databases. Results: Our research results highlight and emphasize the scheme extraction approaches and provide knowledge to researchers and practitioners by proposing schema extraction approaches and their limitations, which contributes to inventing new, more efficient approaches. Conclusion: In our future work, inspired by the recent advances in quantum computing and the emergence of post-quantum cryptography (PQC), we aim to propose a schema extraction approach that blends cutting-edge technologies with a strong focus on database security.

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A Process for Inference of Columnar NoSQL Database Schemas

Cited by 4 publications

References 6 publications

Tabular Data Insights and Synthesis with the AutoTable Approach

Tabular Data Insights and Synthesis with the AutoTable Approach

A universal approach for multi-model schema inference

Schema Extraction in NoSQL Databases: A Systematic Literature Review

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