Extraction of Semantic Links from a Document-Oriented NoSQL Database

The prior declaration of a schema when creating a database (DB) is not necessary for most NoSQL systems. This “Schemaless” property is important since it provides undeniable flexibility during data exploitation. However, the absence of schema is a major obstacle to the expression of precise queries...

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Veröffentlicht in:	SN computer science Jg. 4; H. 2; S. 148
Hauptverfasser:	Abdelhedi, Fatma, Rajhi, Hela, Zurfluh, Gilles
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 09.01.2023 Springer Nature B.V Springer
Schriftenreihe:	Advances on Model-Driven Engineering and Software Development
Schlagworte:	Advances on Model-Driven Engineering and Software Development Case studies Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Disease Documents Information Systems and Communication Service Inheritances Metamodels Motivation Names Original Research Patients Pattern Recognition and Graphics Queries Semantics Software Software development Software engineering Software Engineering/Programming and Operating Systems User needs Vision Logical schema Models transformation NoSQL DB Schemaless QVT Semantic links
ISSN:	2661-8907, 2662-995X, 2661-8907
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Abstract	The prior declaration of a schema when creating a database (DB) is not necessary for most NoSQL systems. This “Schemaless” property is important since it provides undeniable flexibility during data exploitation. However, the absence of schema is a major obstacle to the expression of precise queries on a DB. A new area of research has emerged to allow users of Schemaless NoSQL systems to visualize the schema of the data. Research works have proposed schema extraction processes, but these solutions are generally limited. In our previous works (Abdelhedi et al. in Proceedings of the 10th international conference on model-driven engineering and software development, pp 61–71. https://doi.org/10.5220/0010899000003119 , 2022), we proposed a logical schema extraction process for a document-oriented NoSQL DB to address the needs of a medical application. In this paper, we extend this process to additional relationship types. To do this, we use the model driven architecture which proposes a development method based on metamodeling and the definition of transformation rules. The DB schema is obtained by applying a set of transformation rules to the specifications extracted from the DB. The interest of our process is to produce a schema that allows users of a NoSQL DB to build complex and precise queries. This is useful for both computer scientists who create a large number of complex queries as well as for decision makers who often have difficulty in apprehending the semantic of the data. Our extraction process was implemented in a medical application.
AbstractList	The prior declaration of a schema when creating a database (DB) is not necessary for most NoSQL systems. This “Schemaless” property is important since it provides undeniable flexibility during data exploitation. However, the absence of schema is a major obstacle to the expression of precise queries on a DB. A new area of research has emerged to allow users of Schemaless NoSQL systems to visualize the schema of the data. Research works have proposed schema extraction processes, but these solutions are generally limited. In our previous works (Abdelhedi et al. in Proceedings of the 10th international conference on model-driven engineering and software development, pp 61–71. https://doi.org/10.5220/0010899000003119, 2022), we proposed a logical schema extraction process for a document-oriented NoSQL DB to address the needs of a medical application. In this paper, we extend this process to additional relationship types. To do this, we use the model driven architecture which proposes a development method based on metamodeling and the definition of transformation rules. The DB schema is obtained by applying a set of transformation rules to the specifications extracted from the DB. The interest of our process is to produce a schema that allows users of a NoSQL DB to build complex and precise queries. This is useful for both computer scientists who create a large number of complex queries as well as for decision makers who often have difficulty in apprehending the semantic of the data. Our extraction process was implemented in a medical application. The prior declaration of a schema when creating a database (DB) is not necessary for most NoSQL systems. This “Schemaless” property is important since it provides undeniable flexibility during data exploitation. However, the absence of schema is a major obstacle to the expression of precise queries on a DB. A new area of research has emerged to allow users of Schemaless NoSQL systems to visualize the schema of the data. Research works have proposed schema extraction processes, but these solutions are generally limited. In our previous works (Abdelhedi et al. in Proceedings of the 10th international conference on model-driven engineering and software development, pp 61–71. https://doi.org/10.5220/0010899000003119 , 2022), we proposed a logical schema extraction process for a document-oriented NoSQL DB to address the needs of a medical application. In this paper, we extend this process to additional relationship types. To do this, we use the model driven architecture which proposes a development method based on metamodeling and the definition of transformation rules. The DB schema is obtained by applying a set of transformation rules to the specifications extracted from the DB. The interest of our process is to produce a schema that allows users of a NoSQL DB to build complex and precise queries. This is useful for both computer scientists who create a large number of complex queries as well as for decision makers who often have difficulty in apprehending the semantic of the data. Our extraction process was implemented in a medical application.
ArticleNumber	148
Author	Rajhi, Hela Zurfluh, Gilles Abdelhedi, Fatma
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References	Fruth M, Dauberschmidt K, Scherzinger SJ. Managing schemas for NoSQL document stores. In: IEEE 37th international conference on data engineering (ICDE), 2021. pp. 2693–6. https://doi.org/10.1109/ICDE51399.2021.00306. Laney D. 3D data management: Controlling data volume, velocity and variety. META group research note, 2001. BaaziziM-ALahmarHBColazzoDGhelliGSartianiCSchema inference for massive JSON datasetsExtend Database Technol201710.5441/002/edbt.2017.2107561594 Chillón AH, Hoyos JR, García-Molina J, Ruiz DS. Discovering entity inheritance relationships in document stores. Knowl Based Syst. 2021;230:107394. https://doi.org/10.1016/j.knosys.2021.107394. OMG: XML metadata interchange. https://www.omg.org/spec/XMI/. Accessed 20 Jan 2022. Eclipse: Ecore Tools. https://www.eclipse.org/ecoretools/doc/index.html. Accessed 20 Jan 2022. Baazizi M-A, Colazzo D, Ghelli G, Sartiani C. A type system for interactive JSON schema inference. In: 46th international colloquium on automata, languages, and programming (ICALP), 2019. Ruiz DS, Morales SF, Molina JG. Inferring versioned schemas from NoSQL databases and its applications. In: International conference on conceptual modeling, 2015, pp. 467–480. https://doi.org/10.1007/978-3-319-25264-3_35. MongoDB. https://www.mongodb.com/. Accessed 5 Sept 2021. Istiqamah AN, Wiharja KRS. A schema extraction of document-oriented database for data warehouse. Int J Inf Commun Technol. 2021;7(2):36–47. https://doi.org/10.21108/ijoict.v7i2.584. Abdelhedi F, Rajhi H, Zurfluh G. Extraction process of the logical schema of a document-oriented NoSQL database. In: Proceedings of the 10th international conference on model-driven engineering and software development, 2022, pp. 61–71. https://doi.org/10.5220/0010899000003119. Wang L, Hassanzadeh O, Zhang S, Shi J, Jiao L, Zou J, Wang C. Schema management for document stores. Proc VLDB Endow. 2015;8:922–33. https://doi.org/10.14778/2777598.2777601. BaaziziM-AColazzoDGhelliGSartianiCParametric schema inference for massive JSON datasetsVLDB J201928449752110.1007/s00778-018-0532-707561594 Aftab Z, Iqbal W, Almustafa KM, Bukhari F, Abdullah M. Automatic NoSQL to relational database transformation with dynamic schema mapping. Sci Programm. 2020. https://doi.org/10.1155/2020/8813350. OMG. https://www.omg.org/. Accessed 1 June 2021. OMG. MDA-The architecture of choice for a changing world. https://www.omg.org/mda. Accessed 1 Apr 2021. ODMS: Operational Database Management Systems. http://www.odbms.org/odmg-standard/. Accessed 10 Apr 2021. OMG: MOF query/view/transformation. https://www.omg.org/spec/QVT/1.3/About-QVT/. Accessed 20 Jan 2022. Erwin: Erwin Data Modeler. https://www.erwin.com/products/erwin-data-modeler/. Accessed 2 June 2021. Eclipse: Eclipse Modeling Framework (EMF) https://www.eclipse.org/modeling/emf/. Accessed 10 Jan 2022. OrientDB. https://orientdb.org/. Accessed 10 Apr 2021. Frozza AA, dos Santos Mello R, da Costa FS. An approach for schema extraction of JSON and extended JSON document collections. In: IEEE international conference on information reuse and integration (IRI), 2018. pp. 356–63. https://doi.org/10.1109/IRI.2018.00060. Idera: Data modeling tools for enterprise-scale data architecture. https://www.idera.com/products/er-studio/enterprise-data-modeling. Accessed 2 June 2021. OrientDB: basic concepts. https://orientdb.org/docs/3.0.x/datamodeling/Concepts.html. Accessed 10 Jan 2022. MongoDB. https://www.mongodb.com/products/compass. Accessed 5 Sept 2021. UML. https://www.uml.org/. Accessed 12 Dec 2021. Wang L, Wang J, Wang M, Li Y, Liang Y, Xu D. Using internet search engines to obtain medical information: a comparative study. J Med Internet Res. 2012;14(3):e74. https://doi.org/10.2196/jmir.1943. 1578_CR18 M-A Baazizi (1578_CR5) 2019; 28 1578_CR19 1578_CR10 1578_CR11 1578_CR12 1578_CR13 1578_CR14 1578_CR15 1578_CR16 1578_CR17 M-A Baazizi (1578_CR4) 2017 1578_CR9 1578_CR8 1578_CR21 1578_CR7 1578_CR22 1578_CR6 1578_CR23 1578_CR24 1578_CR25 1578_CR3 1578_CR26 1578_CR2 1578_CR27 1578_CR1 1578_CR20
References_xml	– reference: Laney D. 3D data management: Controlling data volume, velocity and variety. META group research note, 2001. – reference: Istiqamah AN, Wiharja KRS. A schema extraction of document-oriented database for data warehouse. Int J Inf Commun Technol. 2021;7(2):36–47. https://doi.org/10.21108/ijoict.v7i2.584. – reference: Abdelhedi F, Rajhi H, Zurfluh G. Extraction process of the logical schema of a document-oriented NoSQL database. In: Proceedings of the 10th international conference on model-driven engineering and software development, 2022, pp. 61–71. https://doi.org/10.5220/0010899000003119. – reference: OMG. MDA-The architecture of choice for a changing world. https://www.omg.org/mda. Accessed 1 Apr 2021. – reference: Ruiz DS, Morales SF, Molina JG. Inferring versioned schemas from NoSQL databases and its applications. In: International conference on conceptual modeling, 2015, pp. 467–480. https://doi.org/10.1007/978-3-319-25264-3_35. – reference: OrientDB: basic concepts. https://orientdb.org/docs/3.0.x/datamodeling/Concepts.html. Accessed 10 Jan 2022. – reference: MongoDB. https://www.mongodb.com/. Accessed 5 Sept 2021. – reference: Eclipse: Ecore Tools. https://www.eclipse.org/ecoretools/doc/index.html. Accessed 20 Jan 2022. – reference: BaaziziM-AColazzoDGhelliGSartianiCParametric schema inference for massive JSON datasetsVLDB J201928449752110.1007/s00778-018-0532-707561594 – reference: Baazizi M-A, Colazzo D, Ghelli G, Sartiani C. A type system for interactive JSON schema inference. In: 46th international colloquium on automata, languages, and programming (ICALP), 2019. – reference: OMG. https://www.omg.org/. Accessed 1 June 2021. – reference: Fruth M, Dauberschmidt K, Scherzinger SJ. Managing schemas for NoSQL document stores. In: IEEE 37th international conference on data engineering (ICDE), 2021. pp. 2693–6. https://doi.org/10.1109/ICDE51399.2021.00306. – reference: Chillón AH, Hoyos JR, García-Molina J, Ruiz DS. Discovering entity inheritance relationships in document stores. Knowl Based Syst. 2021;230:107394. https://doi.org/10.1016/j.knosys.2021.107394. – reference: BaaziziM-ALahmarHBColazzoDGhelliGSartianiCSchema inference for massive JSON datasetsExtend Database Technol201710.5441/002/edbt.2017.2107561594 – reference: Eclipse: Eclipse Modeling Framework (EMF) https://www.eclipse.org/modeling/emf/. Accessed 10 Jan 2022. – reference: OMG: XML metadata interchange. https://www.omg.org/spec/XMI/. Accessed 20 Jan 2022. – reference: Idera: Data modeling tools for enterprise-scale data architecture. https://www.idera.com/products/er-studio/enterprise-data-modeling. Accessed 2 June 2021. – reference: MongoDB. https://www.mongodb.com/products/compass. Accessed 5 Sept 2021. – reference: OMG: MOF query/view/transformation. https://www.omg.org/spec/QVT/1.3/About-QVT/. Accessed 20 Jan 2022. – reference: ODMS: Operational Database Management Systems. http://www.odbms.org/odmg-standard/. Accessed 10 Apr 2021. – reference: Wang L, Wang J, Wang M, Li Y, Liang Y, Xu D. Using internet search engines to obtain medical information: a comparative study. J Med Internet Res. 2012;14(3):e74. https://doi.org/10.2196/jmir.1943. – reference: UML. https://www.uml.org/. Accessed 12 Dec 2021. – reference: Aftab Z, Iqbal W, Almustafa KM, Bukhari F, Abdullah M. Automatic NoSQL to relational database transformation with dynamic schema mapping. Sci Programm. 2020. https://doi.org/10.1155/2020/8813350. – reference: Wang L, Hassanzadeh O, Zhang S, Shi J, Jiao L, Zou J, Wang C. Schema management for document stores. Proc VLDB Endow. 2015;8:922–33. https://doi.org/10.14778/2777598.2777601. – reference: OrientDB. https://orientdb.org/. Accessed 10 Apr 2021. – reference: Erwin: Erwin Data Modeler. https://www.erwin.com/products/erwin-data-modeler/. Accessed 2 June 2021. – reference: Frozza AA, dos Santos Mello R, da Costa FS. An approach for schema extraction of JSON and extended JSON document collections. In: IEEE international conference on information reuse and integration (IRI), 2018. pp. 356–63. https://doi.org/10.1109/IRI.2018.00060. – ident: 1578_CR3 doi: 10.14778/2777598.2777601 – ident: 1578_CR8 doi: 10.21108/ijoict.v7i2.584 – year: 2017 ident: 1578_CR4 publication-title: Extend Database Technol doi: 10.5441/002/edbt.2017.21 – volume: 28 start-page: 497 issue: 4 year: 2019 ident: 1578_CR5 publication-title: VLDB J doi: 10.1007/s00778-018-0532-7 – ident: 1578_CR2 doi: 10.2196/jmir.1943 – ident: 1578_CR1 doi: 10.5220/0010899000003119 – ident: 1578_CR18 – ident: 1578_CR12 – ident: 1578_CR14 – ident: 1578_CR16 – ident: 1578_CR7 doi: 10.1109/ICDE51399.2021.00306 – ident: 1578_CR22 – ident: 1578_CR20 – ident: 1578_CR9 doi: 10.1155/2020/8813350 – ident: 1578_CR26 – ident: 1578_CR24 – ident: 1578_CR10 doi: 10.1016/j.knosys.2021.107394 – ident: 1578_CR6 doi: 10.1109/IRI.2018.00060 – ident: 1578_CR13 – ident: 1578_CR19 – ident: 1578_CR11 doi: 10.1007/978-3-319-25264-3_35 – ident: 1578_CR17 – ident: 1578_CR15 – ident: 1578_CR27 – ident: 1578_CR21 – ident: 1578_CR23 – ident: 1578_CR25
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