Querying data from the DKG

How does querying of the DKG work?
The DKG querying entails both data discovery and graph querying. Therefore OriginTrail implements a set of protocols for discovery and exchange which cover a wide array of possible scenarios. 
Network query
Querying the DKG is done using the network query API. It is used to look up all datasets containing a specific identifier (such as a supply chain identifier, like a GS1 barcode or RFID value).
The query request is an array values that identify a particular object in a dataset. These identifiers are sent as an array of objects, where the path parameter is the type of identifier (such as ean13, sgtin, sgln, or id for general identifier), value is the identifier value or an array of possible values, and opcode is either EQ or IN, depending on whether the queried object identifier needs to equal or belong to the given value parameter.
Using DKG client
// sending network query
dkg.networkQuery([{
    path: 'sgtin',
    value: 'urn:epc:id:sgtin:111111111',
    opcode: 'EQ'
}]
).then((result)=>{
// result returned will contain network query responses
}
Using the API
POST http://NODE_IP:PORT/api/latest/network/query
{
  "query":
  [
    {
      "path": "sgtin",
      "value": "urn:epc:id:sgtin:111111111",
      "opcode": "EQ"
    }
  ]
}
The returned responses contain query_id which can be used to fetch responses from network query.
To view responses call the query response API, as a parameter use query_id returned from network query API call.
GET http://NODE_IP:PORT/api/latest/network/query/responses/{query_id}
The returned responses contain an array of datasets which contain objects whose identifiers fit the given query. This response can then be used to import a desired dataset on one’s node, which will enable querying the graph locally or exporting and viewing the dataset.
To fetch a dataset included in the response run.
Using DKG client
// fetch, save and export dataset from the network
dkg.remoteFetch([{
    reply_id: '51cceff0-82fd-43c6-98a2-4cd64ca45ff9',
    data_set_id: '0x14fa53714b8b0bca2e0048f5ab715f32edadb2279708d54dd176f5a21820b668',
    options: { standard_id: 'GRAPH' }
}]
).then((result)=>{
// result returned will contain fetched dataset
}
Using the API
POST http://NODE_IP:PORT/api/latest/network/read_export
body {
 form-data {
  "reply_id": "51cceff0-82fd-43c6-98a2-4cd64ca45ff9",
  "data_set_id": "0x14fa53714b8b0bca2e0048f5ab715f32edadb2279708d54dd176f5a21820b668",
  "standard_id": "GRAPH"
 }
}
The returned responses contain handler_id which can be used to download fetched dataset using export_result route (explained in this document).
Local Knowledge Graph querying - Trail
Identifier types and values and trail depth
Querying the local knowledge graph performs a graph traversal starting from a particular vertex in the graph and traversing over the specified edge types.
The result of the trail represents all objects found on the trail (the historical provenance trail spanning all datasets), along with an array that indicates which datasets those objects belong to.
Using DKG client
// get trail
dkg.trail({
        identifier_types: ['ean13'],
        identifier_values: ['123456'],
        connection_types: ['EPC'],
        opcode: 'EQ',
        depth: 5,
        extended: false
}).then((result)=>{
   // retuned result contains trail
}
Using the API
POST http://NODE_IP:PORT/api/latest/trail
​
body {
   raw {
    "identifier_types": ["ean13"],
    "identifier_values": ["123456"],
    "connection_types": ["EPC"],
    "opcode": "EQ",
    "depth": 5,
    "extended": false
  }
}
identifier_types and identifier_values are two arrays used to determine the starting object of the trail traversal. Note that these two arrays must be of the same length, and will be paired in the order they were given (first element of the identifier_types array corresponds to the first element of the identifier_values array, etc).
The depth parameter determines how far from the starting vertex will the traversal go. If the depth is set to 0 the traversal will return only the objects identified by the given parameters.
Connection types
connection_types is an array which serves as a filter in the graph trail traversal operation. When observing a vertex in the graph, only the vertices which are connected to the currently observed vertex by a relation type which is in the connection_types array will be visited and included in the graph.
../_images/connection-example1.png
Example: In the graph pictured above, if the connection_types contained rel_type_1 and not rel_type_2, a traversal starting from vertex B would return vertex A and would not return vertex C.
In order to avoid backtracking in the trail and attaching superfluous information, a vertex will not be visited if the relation types on the path to that vertex are the same two times in a row.
../_images/connection-example2.png
Example: In the graph pictured above, if the connection_types contained rel_type_1, a traversal starting from vertex A would return vertex B and would not return vertex C.
If the connection_types parameter is omitted, the entire graph is traversed (to the specified depth), without the backtracking prevention feature. It should be noted that the knowledge graph can be a highly dense graph, and traversing without filters can return extremely large results and might cause problems with node performance.
Reach parameter
Reach is an optional parameter that can be used to modify which objects are retrieved. When the reach parameter is specified as extended the node will execute the trail, then check which objects are referenced in the trail but are not included in it. These objects are then additionally retrieved from the local knowledge graph and appended to the trail response.
The default behaviour can be explicitly called by setting the reach parameter value to narrow .
Check the developer reference for API details
​
Exporting whole datasets
Datasets can be exported via the export API.
Using DKG client:
// exporting a dataset
dkg.export(dataset_id).then((export_result)=>{
    // result returned will contain requested dataset
});
Using the API
POST http://NODE_IP:PORT/api/latest/export
body{
 raw{
  "dataset_id": "0x317a39f0c0bc7f24e4a27c53fca8d180156f0704351b4fd6454e981e1eb1b0a5",
 }
}
The returned responses contain handler_id which can be used to download exported dataset using export_result route.
GET http://NODE_IP:PORT/api/latest/export/result/{handler_id}
Complex queries
Complex queries are currently supported on the level of the local graph database instance. The workflow for interacting with the DKG is to:
discover information on the network via the network query
remote-fetch information in the local graph DB
query the graph DB locally
The upcoming versions of ot-node will add further support for complex querying (SPARQL, GraphQL, Pathquery), however the intention of the OriginTrail DKG is not to reinvent the wheel, rather leverage existing graph solutions in the space - OriginTrail is about connecting, rather than replacing. To stay up to date, check out the official project roadmap.
​
Querying via specific query interfaces (coming soon)
GraphQL interface
The ability to add GraphQL endpoints will be available in the upcoming version v6.
EPCIS interface
The OT DKG has been supporting the GS1 EPCIS standard and aims at providing an EPCIS interface in the future version. More information on EPCIS interfaces can be found here​
SPARQL interface
The ability to create SPARQL endpoints will be available in the upcoming version v6.

Publishing data to the DKG

Data verification

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On this page

How does querying of the DKG work?

Network query

Local Knowledge Graph querying - Trail

Exporting whole datasets

Complex queries

Querying via specific query interfaces (coming soon)

GraphQL interface

EPCIS interface

SPARQL interface