A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

Yuan, Gongsheng; Lu, Jiaheng; Yan, Zhengtong; Wu, Sai

doi:10.1145/3567444

Cited by 4 publications

(1 citation statement)

References 99 publications

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“…In contrast, the second approach is based on approximate pattern recognition. Papers describing exact pattern recognition procedures are provided in [13]- [19], whereas those describing approximate pattern-discovery approaches are provided in [20]- [22].…”

Section: A Approaches To Schema Discoverymentioning

confidence: 99%

Restoration of Data Structures Using Machine Learning Techniques

Cvijetić,

Radivojević

2023

IEEE Access

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Tabular data is the most common format used to represent real-world information. Almost all programs created for storing or processing data, such as relational database systems, spreadsheets, and statistical analysis software can import or export tabular data. These programs are not sufficiently robust to automatically solve the problems of importing messy delimited files or files that contain data from multiple tables. Additional messy datasets contain data delimited by multiple delimiters without the names of the table columns, and parts of the table rows have substituted or deleted columns. This paper proposes the STCExtract algorithm for reconstructing table structures and data in which the input file can be arranged. The STCExtract algorithm is designed to be domain-independent and modular according to machine learning algorithms and other parameters. The algorithm was developed as a two-phase process, in which the original data tables were recognized in the first phase and the columns of the original data tables in the second phase. The STCExtract algorithm was evaluated through expensive experiments using multiple real datasets. Multiple messy datasets were generated for the four experiments. Three experiments were conducted to determine the optimal parameters for the STCExtract algorithm. A fourth experiment was conducted to evaluate the proposed algorithm. The results show that the STCExtract algorithm correctly arranged the structure of the tables with an accuracy of 94.4% to 100%. The accuracy of the STCExtract algorithm in the second phase (when the data were allocated to columns) ranged from 59.7% to 90.2%.

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Section: A Approaches To Schema Discoverymentioning

confidence: 99%

Restoration of Data Structures Using Machine Learning Techniques

Cvijetić,

Radivojević

2023

IEEE Access

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Property Graphs in Arachne

Rodriguez,

Buschmann,

et al. 2023

2023 IEEE High Performance Extreme Computing Conference (HPEC)

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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 Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

Cited by 4 publications

References 99 publications

Restoration of Data Structures Using Machine Learning Techniques

Restoration of Data Structures Using Machine Learning Techniques

Property Graphs in Arachne

Schema Extraction in NoSQL Databases: A Systematic Literature Review

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