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1. WO2020200404 - METHOD AND QUERY MODULE FOR QUERYING INDUSTRIAL DATA

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS :

1. A method for querying industrial data, including the steps of :

- receiving, by an endpoint of a query module, a first query ex pression from a client, the first query expression being ex pressed by a query language for accessing a semantically en riched and graph-based information model for automation pur poses;

- transforming, by the endpoint, the first query expression into a second query expression, the second query expression being expressed by a query language for accessing a triple store in formation model according to a Resource Description Framework (RDF) format, said transforming including a step of retrieving at least one operand of the first query expression, applying at least one transformation rule for said at least one operand and replacing the at least one operand by at least one state ment of the second query expression;

- using the second query expression for performing, by a query engine, a query on a triple store, the triple store including an aggregated ontology of said industrial data; and;

- returning a query result to the client.

2. The method according to claim 1, wherein said semantically enriched and graph-based information model for automation pur poses is an OPC UA information model.

3. The method according to one of the aforementioned claims, wherein said triple store information model is expressed in an ontology language including OWL, RDF, and RDFS .

4. The method according to one of the aforementioned claims, wherein said second query expression is substantially expressed by SPARQL .

5. The method according to one of the aforementioned claims, wherein the aggregated ontology of said industrial data includes a static portion and a dynamic portion.

6. The method according to claim 5, wherein said static portion includes type-hierarchy data from at least one graph-based in formation model of at least one industrial entity, wherein the type-hierarchy data is transformed into a triple store infor mation model and joined with at least one other transformed type-hierarchy data of at least one other industrial entity data to eventually form the aggregated ontology.

7. The method according to claim 6, wherein said static portion is amended in case that the graph-based information model of at least one of said industrial entities is updated.

8. The method according to claim 7, wherein an update of the graph-based information model of at least one of said industrial entities is announced by an event.

9. The method according to one of the aforementioned claims 5 to 8, wherein said dynamic part of industrial data includes dynamic assignments of at least one data value gathered from at least one industrial entity at runtime in response to a query and wherein said at least one data value is integrated into said ag gregated ontology.

10. The method according to one of the aforementioned claims, wherein generating said aggregated ontology includes the steps of :

- gathering said industrial stored by a graph-based information model for automation purposes amongst industrial entities within an aggregated address space; and;

- transforming the aggregated address space into the triple store information model according to the Resource Description Framework format.

11. A query module comprising:

- a processor; and;

- a data storage device having stored thereon computer executa ble program code, which, when executed by the processor, caus es the processor to:

- receive a first query expression from a client, the first query expression being expressed by a query language for accessing a semantically enriched and graph-based infor mation model for automation purposes;

- transform the first query expression into a second query expression, the second query expression being expressed by a query language for accessing a triple store infor mation model according to a Resource Description Frame work (RDF) format, wherein the transform includes a step of retrieving at least one operand of the first query ex pression, applying at least one transformation rule for said at least one operand and replacing the at least one operand by at least one statement of the second query ex pression;

- use the second query expression for performing a query on a triple store, the triple store including an aggregated ontology of said industrial data; and;

- return a query result to the client.

12. A non-transitory computer-readable storage medium having stored thereon computer executable program code, which, when ex ecuted by a computer, causes the computer to:

receive a first query expression from a client, the first que ry expression being expressed by a query language for access- ing a semantically enriched and graph-based information model for automation purposes;

- transform the first query expression into a second query ex pression, the second query expression being expressed by a query language for accessing a triple store information model according to a Resource Description Framework (RDF) format, wherein the transform includes a step of retrieving at least one operand of the first query expression, applying at least one transformation rule for said at least one operand and re- placing the at least one operand by at least one statement of the second query expression;

- use the second query expression for performing a query on a triple store, the triple store including an aggregated ontolo gy of said industrial data; and;

- return a query result to the client.