Target Data Model

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Slavko Maric - One of the best experts on this subject based on the ideXlab platform.

  • WIMS - An Online Business Process Model-driven Generator of the Conceptual Database Model
    Proceedings of the 8th International Conference on Web Intelligence Mining and Semantics - WIMS '18, 2018
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper presents an online two-phase business process Model-driven generator of the conceptual Database Model. The generator is implemented as a web-based, platform-independent tool, in contrast to the existing tools that are dependent on some specific technological platform used for their implementation. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by a sole concrete notation such as BPMN, the presented generator uses an indirect two-phase approach, which is based on the introduction of a simple domain specific language as an intermediate layer between source and Target notations. The implemented online generator enables automatic generation of the Target Data Model represented by UML class diagram, based on business process Models represented by two concrete notations: BPMN and UML activity diagram.

  • MEDI - An Approach to Automated Two-Phase Business Model-Driven Synthesis of Data Models
    Model and Data Engineering, 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted concepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

  • An approach to automated two-phase business Model-driven synthesis of Data Models
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models repre-sented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted con-cepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

Drazen Brdjanin - One of the best experts on this subject based on the ideXlab platform.

  • WIMS - An Online Business Process Model-driven Generator of the Conceptual Database Model
    Proceedings of the 8th International Conference on Web Intelligence Mining and Semantics - WIMS '18, 2018
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper presents an online two-phase business process Model-driven generator of the conceptual Database Model. The generator is implemented as a web-based, platform-independent tool, in contrast to the existing tools that are dependent on some specific technological platform used for their implementation. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by a sole concrete notation such as BPMN, the presented generator uses an indirect two-phase approach, which is based on the introduction of a simple domain specific language as an intermediate layer between source and Target notations. The implemented online generator enables automatic generation of the Target Data Model represented by UML class diagram, based on business process Models represented by two concrete notations: BPMN and UML activity diagram.

  • MEDI - An Approach to Automated Two-Phase Business Model-Driven Synthesis of Data Models
    Model and Data Engineering, 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted concepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

  • An approach to automated two-phase business Model-driven synthesis of Data Models
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models repre-sented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted con-cepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

Danijela Banjac - One of the best experts on this subject based on the ideXlab platform.

  • WIMS - An Online Business Process Model-driven Generator of the Conceptual Database Model
    Proceedings of the 8th International Conference on Web Intelligence Mining and Semantics - WIMS '18, 2018
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper presents an online two-phase business process Model-driven generator of the conceptual Database Model. The generator is implemented as a web-based, platform-independent tool, in contrast to the existing tools that are dependent on some specific technological platform used for their implementation. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by a sole concrete notation such as BPMN, the presented generator uses an indirect two-phase approach, which is based on the introduction of a simple domain specific language as an intermediate layer between source and Target notations. The implemented online generator enables automatic generation of the Target Data Model represented by UML class diagram, based on business process Models represented by two concrete notations: BPMN and UML activity diagram.

  • MEDI - An Approach to Automated Two-Phase Business Model-Driven Synthesis of Data Models
    Model and Data Engineering, 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted concepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

  • An approach to automated two-phase business Model-driven synthesis of Data Models
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models repre-sented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted con-cepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

Goran Banjac - One of the best experts on this subject based on the ideXlab platform.

  • WIMS - An Online Business Process Model-driven Generator of the Conceptual Database Model
    Proceedings of the 8th International Conference on Web Intelligence Mining and Semantics - WIMS '18, 2018
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper presents an online two-phase business process Model-driven generator of the conceptual Database Model. The generator is implemented as a web-based, platform-independent tool, in contrast to the existing tools that are dependent on some specific technological platform used for their implementation. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by a sole concrete notation such as BPMN, the presented generator uses an indirect two-phase approach, which is based on the introduction of a simple domain specific language as an intermediate layer between source and Target notations. The implemented online generator enables automatic generation of the Target Data Model represented by UML class diagram, based on business process Models represented by two concrete notations: BPMN and UML activity diagram.

  • MEDI - An Approach to Automated Two-Phase Business Model-Driven Synthesis of Data Models
    Model and Data Engineering, 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models represented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted concepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

  • An approach to automated two-phase business Model-driven synthesis of Data Models
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
    Co-Authors: Drazen Brdjanin, Danijela Banjac, Goran Banjac, Slavko Maric
    Abstract:

    The paper proposes an approach to automated two-phase business Model-driven synthesis of the conceptual Database Model. Unlike the existing approaches, which are characterised by the direct synthesis of the Target Model based on business process Models repre-sented by concrete notations (e.g. BPMN or UML activity diagram), the proposed approach is characterised by the introduction of a domain specific language (DSL) as an intermediate between different concrete business Modelling notations and the Target Data Modelling notation. Thus, the Data Model synthesis is split into two phases: (i) extraction of specific concepts from the source business process Model and their DSL-based representation, and (ii) automated generation of the Target Data Model based on the DSL-based representation of the extracted con-cepts. Such an indirect approach could simplify the Target Data Model synthesis and facilitate modifications of the required generator, since all synthesis rules are implemented by one generator that is independent of different source notations in contrast to the existing approaches that require different generators for each source business Modelling notation.

Lisa M. Schilling - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic-ETL: a hybrid approach for health Data extraction, transformation and loading
    BMC Medical Informatics and Decision Making, 2017
    Co-Authors: Michael G. Kahn, Bethany M. Kwan, Traci Yamashita, Elias Brandt, Patrick Hosokawa, Chris Uhrich, Lisa M. Schilling
    Abstract:

    Background Electronic health records (EHRs) contain detailed clinical Data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR Data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other Data sources. The expertise and scalable solutions needed to transform Data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of Data contribution to clinical research networks. Methods We designed and implemented a health Data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous Data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health Data (i.e., “health Data domain experts”) and Target common Data Model. Results D-ETL was implemented to perform ETL operations that load Data from various sources with different Database schema structures into the Observational Medical Outcome Partnership (OMOP) common Data Model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health Data transformation via automatic query generation to harmonize source Datasets. Conclusions D-ETL supports a flexible and transparent process to transform and load health Data into a Target Data Model. This approach offers a solution that lowers technical barriers that prevent Data partners from participating in research Data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health Data.

  • Dynamic-ETL: a hybrid approach for health Data extraction, transformation and loading
    BMC Medical Informatics and Decision Making, 2017
    Co-Authors: Michael G. Kahn, Bethany M. Kwan, Traci Yamashita, Elias Brandt, Patrick Hosokawa, Chris Uhrich, Lisa M. Schilling
    Abstract:

    Electronic health records (EHRs) contain detailed clinical Data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR Data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other Data sources. The expertise and scalable solutions needed to transform Data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of Data contribution to clinical research networks. We designed and implemented a health Data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous Data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health Data (i.e., “health Data domain experts”) and Target common Data Model. D-ETL was implemented to perform ETL operations that load Data from various sources with different Database schema structures into the Observational Medical Outcome Partnership (OMOP) common Data Model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health Data transformation via automatic query generation to harmonize source Datasets. D-ETL supports a flexible and transparent process to transform and load health Data into a Target Data Model. This approach offers a solution that lowers technical barriers that prevent Data partners from participating in research Data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health Data.