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  2. Comparison of data modeling tools - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_data...

    Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect

  3. Elasticsearch - Wikipedia

    en.wikipedia.org/wiki/Elasticsearch

    Elasticsearch is a search engine based on Apache Lucene, a free and open-source search engine. It provides a distributed, multitenant -capable full-text search engine with an HTTP web interface and schema-free JSON documents.

  4. Elastic NV - Wikipedia

    en.wikipedia.org/wiki/Elastic_NV

    [2] [3] It was founded in 2012 in Amsterdam, the Netherlands, and was previously known as Elasticsearch. [ 4 ] The company develops the Elastic Stack— Elasticsearch , Kibana , Beats, and Logstash—previously known as the ELK Stack, [ 5 ] free and paid proprietary features (formerly called X-Pack), Elastic Cloud (a family of SaaS offerings ...

  5. Enterprise data modelling - Wikipedia

    en.wikipedia.org/wiki/Enterprise_data_modelling

    Enterprise data modelling or enterprise data modeling (EDM) is the practice of creating a graphical model of the data used by an enterprise or company. Typical outputs of this activity include an enterprise data model consisting of entity–relationship diagrams (ERDs), XML schemas (XSD), and an enterprise wide data dictionary .

  6. Data modeling - Wikipedia

    en.wikipedia.org/wiki/Data_modeling

    Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston (1997) [7] only two modeling methodologies stand out, top-down and bottom-up: Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data ...

  7. Industry standard data model - Wikipedia

    en.wikipedia.org/wiki/Industry_standard_data_model

    An industry standard data model, or simply standard data model, is a data model that is widely used in a particular industry. The use of standard data models makes the exchange of information easier and faster because it allows heterogeneous organizations to share an agreed vocabulary, semantics, format, and quality standard for data.

  8. Database model - Wikipedia

    en.wikipedia.org/wiki/Database_model

    The inverted file data model can put indexes in a set of files next to existing flat database files, in order to efficiently directly access needed records in these files. Notable for using this data model is the ADABAS DBMS of Software AG, introduced in 1970. ADABAS has gained considerable customer base and exists and supported until today.

  9. Data-driven model - Wikipedia

    en.wikipedia.org/wiki/Data-driven_model

    Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]