The Experts below are selected from a list of 1866 Experts worldwide ranked by ideXlab platform

Wouter Joosen - One of the best experts on this subject based on the ideXlab platform.

  • a workload driven document database schema recommender dbsr
    International Conference on Conceptual Modeling, 2020
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    Database schema design requires careful consideration of the application’s data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for Horizontal Scalability and performance.

  • Object-NoSQL Database Mappers: a benchmark study on the performance overhead
    Journal of Internet Services and Applications, 2017
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and Horizontal Scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies. Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data. In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup. Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM’s search overhead is exceptionally high in comparison to the other ONDMs.

Vincent Reniers - One of the best experts on this subject based on the ideXlab platform.

  • a workload driven document database schema recommender dbsr
    International Conference on Conceptual Modeling, 2020
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    Database schema design requires careful consideration of the application’s data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for Horizontal Scalability and performance.

  • Object-NoSQL Database Mappers: a benchmark study on the performance overhead
    Journal of Internet Services and Applications, 2017
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and Horizontal Scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies. Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data. In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup. Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM’s search overhead is exceptionally high in comparison to the other ONDMs.

Dimitri Van Landuyt - One of the best experts on this subject based on the ideXlab platform.

  • a workload driven document database schema recommender dbsr
    International Conference on Conceptual Modeling, 2020
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    Database schema design requires careful consideration of the application’s data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for Horizontal Scalability and performance.

  • Object-NoSQL Database Mappers: a benchmark study on the performance overhead
    Journal of Internet Services and Applications, 2017
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and Horizontal Scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies. Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data. In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup. Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM’s search overhead is exceptionally high in comparison to the other ONDMs.

Ansar Rafique - One of the best experts on this subject based on the ideXlab platform.

  • a workload driven document database schema recommender dbsr
    International Conference on Conceptual Modeling, 2020
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    Database schema design requires careful consideration of the application’s data model, workload, and target database technology to optimize for performance and data size. Traditional normalization schemes used in relational databases minimize data redundancy, whereas NoSQL document-oriented databases favor redundancy and optimize for Horizontal Scalability and performance.

  • Object-NoSQL Database Mappers: a benchmark study on the performance overhead
    Journal of Internet Services and Applications, 2017
    Co-Authors: Vincent Reniers, Dimitri Van Landuyt, Ansar Rafique, Wouter Joosen
    Abstract:

    In recent years, the hegemony of traditional relational database management systems (RDBMSs) has declined in favour of non-relational databases (NoSQL). These database technologies are better adapted to meet the requirements of large-scale (web) infrastructures handling Big Data by providing elastic and Horizontal Scalability. Each NoSQL technology however is suited for specific use cases and data models. As a consequence, NoSQL adopters are faced with tremendous heterogeneity in terms of data models, database capabilities and application programming interfaces (APIs). Opting for a specific NoSQL database poses the immediate problem of vendor or technology lock-in. A solution has been proposed in the shape of Object-NoSQL Database Mappers (ONDMs), which provide a uniform abstraction interface for different NoSQL technologies. Such ONDMs however come at a cost of increased performance overhead, which may have a significant economic impact, especially in large distributed setups involving massive volumes of data. In this paper, we present a benchmark study quantifying and comparing the performance overhead introduced by Object-NoSQL Database Mappers, for create, read, update and search operations. Our benchmarks involve five of the most promising and industry-ready ONDMs: Impetus Kundera, Apache Gora, EclipseLink, DataNucleus and Hibernate OGM, and are executed both on a single node and a 9-node cluster setup. Our main findings are summarised as follows: (i) the introduced overhead is substantial for database operations in-memory, however on-disk operations and high network latency result in a negligible overhead, (ii) we found fundamental mismatches between standardised ONDM APIs and the technical capabilities of the NoSQL database, (iii) search performance overhead increases linearly with the number of results, (iv) DataNucleus and Hibernate OGM’s search overhead is exceptionally high in comparison to the other ONDMs.

Theo Härder - One of the best experts on this subject based on the ideXlab platform.

  • XQuery processing over NoSQL stores
    2013
    Co-Authors: Henrique Valer, Caetano Sauer, Theo Härder
    Abstract:

    Using NoSQL stores as storage layer for the execution of declarative query processing using XQuery provides a highlevel interface to process data in an optimized manner. NoSQL refers to a plethora of new stores which essentially trades off well-known ACID properties for higher availability or Scalability, using techniques such as eventual consistency, Horizontal Scalability, efficient replication, and schema-less data models. This work proposes a mapping from the data model of different kinds of NoSQL stores—key/value, columnar, and document-oriented—to the XDM data model, allowing for standardization and querying NoSQL data using higherlevel languages, such as XQuery. This work also explores several optimization scenarios to improve performance on top of these stores. Besides, we also add updating semantics to XQuery by introducing simple CRUD-enabling functionalities. Finally, this work analyzes the performance of the system in several scenarios

  • XQuery processing over NoSQL stores
    CEUR Workshop Proceedings, 2013
    Co-Authors: Henrique Valer, Caetano Sauer, Theo Härder
    Abstract:

    Using NoSQL stores as storage layer for the execution of declarative query processing using XQuery provides a highlevel interface to process data in an optimized manner. The term NoSQL refers to a plethora of new stores which essentially trades off well-known ACID properties for higher availability or Scalability, using techniques such as eventual consistency, Horizontal Scalability, efficient replication, and schema-less data models. This work proposes a mapping from the data model of different kinds of NoSQL stores-key/value, columnar, and document-oriented- to the XDM data model, thus allowing for standardization and querying NoSQL data using higher-level languages, such as XQuery. This work also explores several optimization scenarios to improve performance on top of these stores. Besides, we also add updating semantics to XQuery by introducing simple CRUD-enabling functionalities. Finally, this work analyzes the performance of the system in several scenarios.