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Added first draft of detailed example
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- The jpg files ought to be moved elsewhere at a later stage
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Petra Selmer committed Aug 4, 2017
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== Examples

The following examples are intended to show how multiple graphs may be used, and focus on syntax.
We show a fully worked-through example <<complete-example, here>>, describing and illustrating every step of the pipeline in detail.

=== A template for a multiple graph pipeline
[source, cypher]
----
Expand Down Expand Up @@ -446,7 +449,7 @@ INTO NEW GRAPH rollup {
RETURN GRAPHS rollup
----

=== A more complex pipeline: using and materializing multiple graphs
=== A more complex pipeline: using and persisting multiple graphs

[source, cypher]
----
Expand Down Expand Up @@ -486,6 +489,214 @@ INTO NEW GRAPH swedish_triangles {
RETURN count(p) AS num_triangles GRAPHS swedish_triangles, sweden_people, german_people
----

[[complete-example]]
=== A complete example illustrating a data integration scenario

Assume we have two graphs, *ActorsFilmsCities* and *Events*, each of which is contained in a separate location.
This example will show how these two graphs can be integrated into a single graph.

The *ActorsFilmsCities* graph models actors and people fulfilling other roles in the film-industry; films in which they acted, or directed, or for which they wrote the soundtrack; cities in which they were born; and their relationships to family members and colleagues.

Each node is labelled and contains one or two properties (where `YOB` stands for 'year of birth'), and each relationship of type `ACTED_IN` has a `charactername` property indicating the name of the character the relevant `Actor` played in the `Film`.

image::opencypher-PersonActorCityFilm-graph.jpg[Graph,800,700]

The other graph, *Events*, models information on events.
Each event is linked to an event type by an `IS_A` relationship, to a year by an `IN_YEAR` relationship, and to a city by an `IN_CITY` relationship.
For example, the _Battle of Britain_ event is classified as a _War Event_, occurred in the year _1940_, and took place in _London_.

In contrast to the *ActorsFilmsCities* graph, *Events* contains no labels on any node, no properties on any relationship, and only a single `value` property on each node.
*Events* can be considered to be a snapshot of data from an RDF graph, in the sense that every node has one and only one value; i.e. in contrast to a property graph, an RDF graph has properties on neither nodes nor relationships.
(For easier visibility, we have coloured accordingly the cities and city-related relationships, event types and event-type relationships, and year and year-related relationships.)

image::opencypher-Events-graph.jpg[Graph,800,600]

The aims of the data integration exercise are twofold:

* Create and persist to disk (for future use) a new graph, *PersonCityEvents*, containing an amalgamation of data from *ActorsFilmsCities* and *Events*.
*PersonCityEvents* must contain all the event information from *Events*, and only `Person` nodes connected to `City` nodes from *ActorsFilmsCities*.

* Create and return a temporary graph, *Temp-PersonCityCrimes*.
*Temp-PersonCityCrimes* must contain a subset of the data from *PersonCityEvents*, consisting only of the criminal events, their associated `City` nodes, and `Person` nodes associated with the `City` nodes.

==== Step 1:

The first action to take in our data integration exercise is to set the source graph to *ActorsFilmsCities*, for which we need to provide the physical address:

[source, cypher]
----
FROM GRAPH ActorsFilmsCities AT 'graph://actors_films_cities...'
----

Next, match all `Person` nodes who have a `BORN_IN` relationship to a `City`:

[source, cypher]
----
MATCH (p:Person)-[:BORN_IN]->(c:City)
----

Create the new graph *PersonCityEvents*, persist it to _some-location_, and set it as the target graph:

[source, cypher]
----
INTO NEW GRAPH PersonCityEvents AT 'some-location'
----

Write the subgraph induced by the `MATCH` clause above into *PersonCityEvents*:

[source, cypher]
----
CREATE XXXX TODO
----

Putting all these statements together, we get:

_Query sequence for Step 1_:
[source, cypher]
----
FROM GRAPH ActorsFilmsCities AT 'graph://actors_films_cities...'
MATCH (p:Person)-[:BORN_IN]->(c:City)
INTO NEW GRAPH PersonCityEvents AT 'some-location' {
CREATE XXX TODO
}
//Discard all tabular data and cardinality
WITH GRAPHS *
----

At this stage, *PersonCityEvents* is given by:

image::opencypher-PersonCity-graph.jpg[Graph,800,700]

==== Step 2:

The next stage in the pipeline is to add the events information from *Events* to *PersonCityEvents*.

Firstly, the source graph is set to *Events*, for which we need to provide the physical address:

[source, cypher]
----
FROM GRAPH Events AT 'graph://events...'
----

At this point, the *Events* graph is in scope.

All the events information -- the event itself, its type, the year in which it occurred, and the city in which it took place -- is matched:

[source, cypher]
----
MATCH (c)<-[:IN_CITY]-(e)-[:IN_YEAR]->(y),
(e)-[:IS_A]->(et)
----

The target graph is set to the *PersonCityEvents* graph (created earlier):

[source, cypher]
----
INTO GRAPH PersonCityEvents
----

Using the results from the `MATCH` clause, create a subgraph with more intelligible semantics through the transformation of the events information into a less verbose form through greater use of node-level properties.
Write the subgraph to *PersonCityEvents*.

[source, cypher]
----
CREATE XXXX TODO
----

Putting all these statements together, we get:

_Query sequence for Step 2_:
[source, cypher]
----
FROM GRAPH Events AT 'graph://events...'
MATCH (c)<-[:IN_CITY]-(e)-[:IN_YEAR]->(y),
(e)-[:IS_A]->(et)
INTO GRAPH PersonCityEvents {
CREATE XXX TODO
}
//Discard all tabular data and cardinality
WITH GRAPHS *
----

*PersonCityEvents* now contains the following data:

image::opencypher-PersonCityEvents-graph.jpg[Graph,800,700]

==== Step 3:

The last step in the data integration pipeline is the creation of a new, temporary graph, *Temp-PersonCityCrimes*, which is to be populated with the subgraph of all the criminal events and associated nodes from *PersonCityEvents*.

Set *PersonCityEvents* to be in scope:

[source, cypher]
----
FROM GRAPH PersonCityEvents
----

Next, obtain the subgraph of all criminal events -- i.e. nodes labelled with `CriminalEvent` -- and their associated `City` nodes, and `Person` nodes associated with the `City` nodes:

[source, cypher]
----
MATCH (ce:CriminalEvent)-[:HAPPENED_IN]->(c:City)<-[:BORN_IN]-(p:Person)
----

Create the new, temporary graph *Temp-PersonCityCrimes*, and set it as the target graph:

[source, cypher]
----
INTO NEW GRAPH Temp-PersonCityCrimes
----

Write the subgraph acquired earlier to *Temp-PersonCityCrimes*.

[source, cypher]
----
CREATE XXXX TODO
----

Putting all these statements together, we get:

_Query sequence for Step 3_:
[source, cypher]
----
FROM GRAPH PersonCityEvents
MATCH (ce:CriminalEvent)-[:HAPPENED_IN]->(c:City)<-[:BORN_IN]-(p:Person)
INTO NEW GRAPH Temp-PersonCityCrimes {
CREATE XXX TODO
}
----

And, as the final step of the entire data integration pipeline, return *Temp-PersonCityCrimes*, which is comprised of the following data:

image::opencypher-PersonCityCriminalEvents-graph.jpg[Graph,800,700]

The full data integration query pipeline is given by:

[source, cypher]
----
FROM GRAPH ActorsFilmsCities AT 'graph://actors_films_cities...'
MATCH (p:Person)-[:BORN_IN]->(c:City)
INTO NEW GRAPH PersonCityEvents AT 'some-location' {
CREATE XXX TODO
}
WITH GRAPH *
FROM GRAPH Events AT 'graph://events...'
MATCH (c)<-[:IN_CITY]-(e)-[:IN_YEAR]->(y),
(e)-[:IS_A]->(et)
INTO GRAPH PersonCityEvents {
CREATE XXX TODO
}
WITH GRAPH *
FROM GRAPH PersonCityEvents
MATCH (ce:CriminalEvent)-[:HAPPENED_IN]->(c:City)<-[:BORN_IN]-(p:Person)
INTO NEW GRAPH Temp-PersonCityCrimes {
CREATE XXX TODO
}
RETURN GRAPH Temp-PersonCityCrimes
----

== Interaction with existing features

This proposal is far reaching as it changes both the property graph model and the execution model of the language.
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