Flexible data model for integral data management
The flexible data model of GeolinQ the core the integration of the data management chain. The flexible data model acts as the cement between the processes for collecting, integrating and distributing data in the data management chain. The flexible data model links easily source data from different data sources together to provide information products. Source data and information products are easily disclosed via services to applications and end users.
Completely object oriented
The flexible data model in GeolinQ is completely object oriented. The structure of the data managed in a dataset and the metadata of the dataset are defined by class definitions. The attributes of the class definitions describe the data structure and the metadata of the dataset. Class definitions support inheritance in order to allow that different types of objects are stored in the same dataset.
Management of data in datasets
GeolinQ holds geographic and administrative data in datasets. Three types of datasets are available in the GeolinQ:
- Point datasets for grid data and point clouds
- Feature datasets for feature or vector data
- Datasets for administrative data
Grids are data stored in a regular grid such as aerial photos. Point clouds are individual measuring points at a random location, such as LiDAR height data. Grid and point clouds are automatically divided into tiles during import for an outstanding performance at any scale level.
Feature or vector data is data with a geographic component (a geometry) and attribute values. Administrative data is data with attribute values but without a geographic component. Feature and administrative datasets can be linked to other feature and administrative datasets through the use of references.
Mutations and historical data
In GeolinQ, mutations and historical data can be recorded in the flexible data model. Historical data with transactions can be imported into GeolinQ in a dataset for which a transaction key and history attributes such as transaction date or start and end date have been configured. For a dataset with mutations, all changes made by users are automatically registered. For a dataset with mutations, the current situation, the status on a chosen date and the history of processed transactions can be displayed.
Compose information products by using view datasets
With view data sets, information products are directly derived from source data sets with administrative and feature data. Attributes of the view dataset are associated with the attributes of the source dataset through expressions. Functions can be used in the expressions, for example to calculate a buffer, area or length of a geometry. Via the references of a source dataset, the attributes from referenced datasets can easily be applied in the expressions. Conditional expressions can also be included in the view data set to filter the source data.
By grouping the attributes a pivot table can be defined in the view dataset. With a pivot table, source data can be aggregated using aggregation functions such as summing, counting, averaging, and standard deviation of attribute values.
Merge data by using union data sets
Datasets that share the same class definition are combined with uniondatasets into one dataset. All datasets that share the same class definition can be easily selected and merged into the uniondataset.
Merge datasets with the Seamless Point Surface (SPS)
A data collection of raster data such as aerial photographs or depth measurements usually consists of many overlapping datasets with a shared metadata scheme. With the "Seamless Point Surface" (SPS), the grid data sets can be merged into one contiguous data set without overlap based on a selection and priority rules based on the metadata.
In the SPS dataset, only the envelopes of the source datasets are stored so that the underlying points or grids of the source datasets that make up the SPS are retrieved when the SPS dataset is queried. The point and raster data is therefore not stored redundantly, so that the overhead of an SPS is minimal and can be quickly updated with new source data sets.
Dependencies between datasets
The SPS dataset, the view dataset and the union dataset depend on the underlying source datasets. In GeolinQ, the dependencies between datasets (SPS dataset, viewdataset and uniondataset) are recorded and made transparent to the user. If underlying datasets of an SPS, union or view dataset change, the derived datasets are automatically updated by GeolinQ in the same transaction. In this way, the data in the flexible data model always remains consistent and up-to-date.
Discover the value of data management chain integration for your organisation using the flexible data model of GeolinQ