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Difference between revisions of "TPTPModel"
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Most of the model's code resides in the [http://dev.eclipse.org/viewcvs/index.cgi/platform/org.eclipse.tptp.platform.models/?cvsroot=TPTP_Project org.eclipse.tptp.platform.models] plugin. | Most of the model's code resides in the [http://dev.eclipse.org/viewcvs/index.cgi/platform/org.eclipse.tptp.platform.models/?cvsroot=TPTP_Project org.eclipse.tptp.platform.models] plugin. | ||
− | The model infrastructure covers areas like event | + | The model infrastructure covers areas like event loading (input data normalization), data manipulation, query, analysis and persistence. |
Since [[TPTP 4.0]] till current version [[TPTP 4.3]], [[EMF]] has been used for the meta-model, model and persistence layer implementation. | Since [[TPTP 4.0]] till current version [[TPTP 4.3]], [[EMF]] has been used for the meta-model, model and persistence layer implementation. | ||
Revision as of 11:52, 30 November 2006
Contents
Overview
This page is the entry point for informations and discussions around TPTP data models and model infrastructure.
TPTP project provides several data models (for the different domains covered by TPTP) and mechanisms to populate, query, analyze and report on those models.
Here is the list of models currently available, please follow the links to get more information for each model:
- Logging model (Common base event)
- Hierachy model (contains root objects and their relationships)
Most of the model's code resides in the org.eclipse.tptp.platform.models plugin.
The model infrastructure covers areas like event loading (input data normalization), data manipulation, query, analysis and persistence. Since TPTP 4.0 till current version TPTP 4.3, EMF has been used for the meta-model, model and persistence layer implementation.
Event loading layer
Data persistence layer
The TPTP data persistence layer uses today (in TPTP 4.3) extensions of the XML and XMI EMF resources and also provides a generic mechanism to store EMF instances in a database (Derby being the one supported by default), mechanism leveraged in the large log scenario.