Data Cube Integrity Check
A confirmation of the consistent structure of the dataset
The table below shows a live report on the status of the integrity of the currently saved dataset or data cube of LOMBuDa.at according to the used RDF Data Cube Vocabulary by W3C. This integrity is checked via various predefined constraints, which are listed in the presented table and are executed automatically via prewritten SPARQL ASK queries. To have a look at those queries, click on “download query” in the respective row of the table. In a nutshell, this page verifies and shows the semantic consistency of the dataset at a glance.
| IC-# | Brief Description of the Integrity Constraint | Status | SPARQL Query |
|---|---|---|---|
| IC-01 | Unique DataSet | OK | DOWNLOAD QUERY |
| IC-02 | Unique DSD | OK | DOWNLOAD QUERY |
| IC-031) | DSD includes measure | OK | DOWNLOAD QUERY |
| IC-04 | Dimensions have range | OK | DOWNLOAD QUERY |
| IC-05 | Concept dimensions have code lists | OK | DOWNLOAD QUERY |
| IC-06 | Only attributes may be optional | OK | DOWNLOAD QUERY |
| IC-07 | Slice Keys must be declared | OK | DOWNLOAD QUERY |
| IC-081) | Slice Keys consistent with DSD | OK | DOWNLOAD QUERY |
| IC-09 | Unique slice structure | OK | DOWNLOAD QUERY |
| IC-10 | Slice dimensions complete | OK | DOWNLOAD QUERY |
| IC-11 | All dimensions required | OK | DOWNLOAD QUERY |
| IC-12 | No duplicate observations | OK | DOWNLOAD QUERY |
| IC-13 | Required attributes | OK | DOWNLOAD QUERY |
| IC-14 | All measures present | OK | DOWNLOAD QUERY |
| IC-15 | Measure dimension consistent | OK | DOWNLOAD QUERY |
| IC-16 | Single measure on measure dimension observation | OK | DOWNLOAD QUERY |
| IC-17 | All measures present in measures dimension cube | OK | DOWNLOAD QUERY |
| IC-18 | Consistent data set links | OK | DOWNLOAD QUERY |
| IC-19a | Codes from code list (1) | OK | DOWNLOAD QUERY |
| IC-19b | Codes from code list (2) | OK | DOWNLOAD QUERY |
| IC-19c2) | Codes from code list (3) | OK | DOWNLOAD QUERY |
| IC-203) | Codes from hierarchy | OK | DOWNLOAD QUERY |
| IC-213) | Codes from hierarchy (inverse) | OK | DOWNLOAD QUERY |
- These integrity constraints have been changed to work also on an “abbreviated” data cube - in fact, in their current form they are stricter composed as specified.
- This integrity constraint was introduced in addition to the other ones to check specifics of this application's data set in more detail - it does not origin from the W3C.
- These integrity constraints are not executed to their full extend as specified by the W3C. However, as they do not apply to the application's data set (as the checked elements are not used at all), they will always validate successfully.
What are “integrity constraints”?
As specified in the W3C Recommendation of the used RDF Data Cube Vocabulary, an instance of such a data cube (like the one of LOMBuDa.at) should conform to a set of integrity constraints in order to be valid according to this vocabulary. For example, it is tested whether all defined and required attributes are present for each data record. These integrity constraints are defined as narrative prose and SPARQL ASK queries by the W3C. Indeed, the latter ones are used for the checks presented on this very page.
Go to the integrity constraints' definition by the W3CWhat is a data cube?
The data set of LOMBuDa.at is organised as a so-called “data cube”. This is a multi-dimensional array of values, commonly used to represent data along some measures of interest. Every dimension of the cube represents a new measure; whereas the cells in the cube represent the facts of interest or the data records, respectively.
Learn more about this application's data cubeWhat is an “abbreviated” cube?
The used RDF Data Cube Vocabulary
allows to associate common parts of data for a set of observations (dimensions, attributes & measurands) to other, higher levels of the data
structure (like in an observation group or data slice). This prevents possible overheads in transmission and storage through less data redundancies
in such cubes. Such a data cube - like the one of LOMBuDa.at
- is called “abbreviated”.
In contrast, if all data of a cube is attached to each and every data record, even if this leads to redundancy within the data structure, it is called
“normalised”.