could you please advise how to build "if statement" in SPSS Modeler if we have two data sources?
One data source (1) is a table (an output node generated by SPSS Modeler) where all the IDs are listed with which we need to work further.
Another data source (2) is an Excel file where all the IDs are listed whereas this list includes some IDs from (1) but also some additional ones - to all these IDs are assigned values that are needed to be added to the data source (1) not necessarily to the table.
So if the ID from (1) is in (2) we would like to assign a value from (2) to the ID in (1) and have it stored in some table or even better in a file.
Thank you very much for your help / advice.
Patricia
Based on your problem it sounds like you want to merge these datasets. This can be easily done in Modeler via the Merge Node, just make sure the variables have the same name or Modeler won't recognize it as a key. You can see an example here
You can also create a flag variable using the Derive node, see example here
You will have to use the Merge Node to combine the 2 datasets but you don't have to give the same name for the keys IDs. You can use the option condition in the Merge Node without the necessity of having the same name and even the same type of variable.
Syntax example for the merge Node - option condition: 'ID' = 'id'
Related
I am attempting to setup a new graph database to contain records of products and their relationship on each other's versioned components. For each product it can have many components, and each component is made up of multiple versions. Each version can be dependent on none or many versions of any other components. I want to be able to query this database to pick any version of a component and determine what other versioned components it is depended on, or what depends on it.
The data structure I have attempted in my examples is not defined yet, so if a completely different structure is more suitable I'm open to changing it. I originally considered setting the DEPENDS_ON relationship directly between members. However, as new members will be added over time if a new member is added and falls within the version_min and version_max range of an existing records dependancy range, I would then need to go back and identify all affected records and update all of them, which doesn't feel like it would scale over time. This is what lead to the idea of having a member being dependent on a component, with the version limits defined in the relationship parameters.
I have put together a very simple example of 3 products (sample data at the end), with a single type of component and 1 version of each in all cases except one. I've then added only two dependencies into this example, 'a' depends on a range of 'b' versions, and one of the 'b' versions depends on a version of 'c'.
I would like to be able to perform a query to say "give me all downstream members which member prod_a_comp_1_v_1 depends on". Similarly I would like to do this in reverse too, which I imagine is achieved by just reversing some of the relationship parameters.
So far I've achieved this for a single hop (list b versions which a depends on), shown here:
MATCH
p=(a:member{name:'prod_a_comp_1_v_1'})-[d:DEPENDS_ON]->(c:component)<-[v:VERSION_OF]-(b:member) WHERE b.version >= d.version_min AND b.version <= d.version_max
RETURN p
But I don't know how to get it to recursively perform this query on the results of this first match. I investigated variable length/depths, but because there is a conditional parameter in the relationship in the variable depth (DEPENDS_ON), I could not get this to work.
From the example data if querying all downstream dependencies of prod_a_comp_1_v_1 it should return: [prod_b_comp_1_v_2, prod_b_comp_1_v_3, prod_c_comp_1_v_1].
e.g. this figure:
Currently my thought is to use the above query and perform the repeated call on the database based on the results from the client end (capturing circular loops etc.), but that seems very undesirable.
Sample data:
CREATE
(prod_a:product {name:'prod_a'}),
(prod_a_comp_1:component {name: 'prod_a_comp_1', type:'comp_1'}),
(prod_a_comp_1)-[:COMPONENT_OF {type:'comp_1'}]->(prod_a),
(prod_a_comp_1_v_1:member {name:'prod_a_comp_1_v_1', type:'comp_1', version:1}),
(prod_a_comp_1_v_1)-[:VERSION_OF {version:1}]->(prod_a_comp_1)
CREATE
(prod_b:product {name:'prod_b'}),
(prod_b_comp_1:component {name: 'prod_b_comp_1', type:'comp_1'}),
(prod_b_comp_1)-[:COMPONENT_OF {type:'comp_1'}]->(prod_b),
(prod_b_comp_1_v_1:member {name:'prod_b_comp_1_v_1', type:'comp_1', version:1}),
(prod_b_comp_1_v_2:member {name:'prod_b_comp_1_v_2', type:'comp_1', version:2}),
(prod_b_comp_1_v_3:member {name:'prod_b_comp_1_v_3', type:'comp_1', version:3}),
(prod_b_comp_1_v_1)-[:VERSION_OF {version:1}]->(prod_b_comp_1),
(prod_b_comp_1_v_2)-[:VERSION_OF {version:2}]->(prod_b_comp_1),
(prod_b_comp_1_v_3)-[:VERSION_OF {version:3}]->(prod_b_comp_1)
CREATE
(prod_c:product {name:'prod_c'}),
(prod_c_comp_1:component {name: 'prod_c_comp_1', type:'comp_1'}),
(prod_c_comp_1)-[:COMPONENT_OF {type:'comp_1'}]->(prod_c),
(prod_c_comp_1_v_1:member {name:'prod_c_comp_1_v_1', type:'comp_1', version:1}),
(prod_c_comp_1_v_1)-[:VERSION_OF {version:1}]->(prod_c_comp_1)
CREATE
(prod_a_comp_1_v_1)-[:DEPENDS_ON {version_min:2, version_max:3}]->(prod_b_comp_1),
(prod_b_comp_1_v_3)-[:DEPENDS_ON {version_min:1, version_max:100}]->(prod_c_comp_1)
Figure showing full sample data set:
Apologies if I have missunderstood your question but I believe this may be possible with the APOC Expand Paths function: https://neo4j.com/docs/apoc/current/graph-querying/expand-paths/
Example Cypher for your graph:
MATCH (a:member{name:'prod_a_comp_1_v_1'})
CALL apoc.path.expand(a, ">DEPENDS_ON|<VERSION_OF", null, 1, -1)
YIELD path
RETURN path, length(path) AS hops
ORDER BY hops;
Example Results:
I am working on Panama dataset using Neo4J graph database 1.1.5 web version. I identified Ion Sturza, former Prime Minister of Moldova on the database and want to make a map of his related network. I used following code to query using Cypher (creating a variable 'IonSturza'):
MATCH (IonSturza {name: "Ion Sturza"}) RETURN IonSturza
I identified that the entity 'CONSTANTIN LUTSENKO' linked differently to entities like 'Quade..' and 'Kinbo...' with a name in small letters as in this picture. I hence want to map a relationship 'SAME_COMPANY_AS' between the capslock and the uncapped version. I tried the following code based on this answer by #StefanArmbruster:
MATCH (a:Officer {name :"Constantin Lutsenko"}),(b:Officer{name :
"CONSTANTIN LUTSENKO"})
where (a:Officer{name :"Constantin Lutsenko"})-[:SHAREHOLDER_OF]->
(b:Entity{id:'284429'})
CREATE (a)-[:SAME_COMPANY_AS]->(b)
Instead of indexing, I used the 'where' statement to specify the uncapped version which is linked only to the entity bearing id '284429'.
My code however shows the cartesian product error message:
This query builds a cartesian product between disconnected patterns.If a part of a query contains multiple disconnected patterns, this will build a cartesian product between all those parts. This may produce a large amount of data and slow down query processing. While occasionally intended, it may often be possible to reformulate the query that avoids the use of this cross product, perhaps by adding a relationship between the different parts or by using OPTIONAL MATCH (identifier is: (b))<<
Also when I execute, there are no changes, no rows!! What am I missing here? Can someone please help me with inserting this relationship between the nodes. Thanks in advance!
The cartesian product warning will appear whenever you're matching on two or more disconnected patterns. In this case, however, it's fine, because you're looking up both of them by what is likely a unique name, s your result should be one node each.
If each separate part of that pattern returned multiple nodes, then you would have (rows of a) x (rows of b), a cartesian product between the two result sets.
So in this particular case, don't mind the warning.
As for why you're not seeing changes, note that you're reusing variables for different parts of the graph: you're using variable b for both the uppercase version of the officer, and for the :Entity in your WHERE. There is no node that matches to both.
Instead, use different variables for each, and include the :Entity in your match. Also, once you match to nodes and bind them to variables, you can reuse the variable names later in your query without having to repeat its labels or properties.
Try this:
MATCH (a:Officer {name :"Constantin Lutsenko"})-[:SHAREHOLDER_OF]->
(:Entity{id:'284429'}),(b:Officer{name : "CONSTANTIN LUTSENKO"})
CREATE (a)-[:SAME_COMPANY_AS]->(b)
Though I'm not quite sure of what you're trying to do...is an :Officer a company? That relationship type doesn't quite seem right.
I tried the answer by #InverseFalcon and thanks to it, by modifying the property identifier from 'id' to 'name' and using the property for both 'a' and 'b', 4 relationships were created by the following code:
MATCH (a:Officer {name :"Constantin Lutsenko"})-[:SHAREHOLDER_OF]->
(:Entity{name:'KINBOROUGH PORTFOLIO LTD.'}),(b:Officer{name : "CONSTANTIN
LUTSENKO"})-[:SHAREHOLDER_OF]->(:Entity{name:'Chandler Group Holdings Ltd'})
CREATE (a)-[:SAME_NAME_AS]->(b)
Thank you so much #InverseFalcon!
I have imported a csv file into neo4j. I have been trying to define a large number of properties (all the columns) for each node. How can i do that without having to type in each name?
I have been trying this:
USING PERIODIC COMMIT
load csv WITH headers from "file:///frozen_catalog.csv" AS line
//Creating nodes for each product id with its properties
CREATE (product:product{id : line.`o_prd`,
Gross_Price_Average: TOINT(line.`Gross_Price_Average`),
O_PRD_SPG: TOINT(line.`O_PRD_SPG`)});
You can adding properties from maps. For example:
LOAD CSV WITH HEADERS FROM "http://data.neo4j.com/northwind/products.csv" AS row
MERGE (P:Product {productID: row.productID})
SET P += row
http://neo4j.com/docs/developer-manual/current/cypher/clauses/set/#set-adding-properties-from-maps
The LOAD CSV command cannot perform automatic type conversion to ints on certain fields, that must be done explicitly (though you can avoid having to explicitly mention all other fields by using the map projection feature to transform your line data before setting it via stdob--'s suggestion).
You may want to take a look at Neo4j's import tool, as this will allow you to specify field type in headers, which should perform type conversion for you.
That said, 77 columns is a lot of data to all store on individual nodes. You may want to take another look at your data and figure out if some of those properties would be better modeled as nodes with their own label with relationships to your product nodes. You mentioned some of these were categorical properties. Categories are well suited to be modeled separately as nodes instead of as properties, and maybe some of your other properties would work better as nodes as well.
I am trying to dump some date to Neo4J. Some of my node names (in the chosen format for dumping) has numbers, which have to be exported as node-names.
I encounter the following error when the node name or label starts with a number.
Neo.ClientError.Statement.InvalidSyntax
MERGE (1:User {name: "u1"})
Is this because, internally neo4j has a unique ID?. How do we circumvent this problem?
I believe these are just the syntax rules Neo4j uses. Also keep in mind that the thing you are referring to as the node name (1, in your example) is actually a variable name, and only persists for the duration of the query (or until it leaves scope if not carried over in a WITH clause to the next part of the query).
From the developer documentation:
Variable names are case sensitive, and can contain underscores and
alphanumeric characters (a-z, 0-9), but must always start with a
letter...The same rules apply to property names.
While I didn't see anything about label names, it looks like it follows the same syntax rules.
Property values, of course, can be anything you want.
You described the limitation as a "problem", so I'm guessing there's a perceived issue with this in your import, likely around the confusion between variables and what you called node names. If that's so, then please add some more details to your description, and I can add on to my answer accordingly.
We are working on a system where users can define their own nodes and connections, and can query them with arbitrary queries. A user can create a "branch" much like in SCM systems and later can merge back changes into the main graph.
Is it possible to create an efficient data model for that in Neo4j? What would be the best approach? Of course we don't want to duplicate all the graph data for every branch as we have several million nodes in the DB.
I have read Ian Robinson's excellent article on Time-Based Versioned Graphs and Tom Zeppenfeldt's alternative approach with Network versioning using relationnodes but unfortunately they are solving a different problem.
I Would love to know what you guys think, any thoughts appreciated.
I'm not sure what your experience level is. Any insight into that would be helpful.
It would be my guess that this system would rely heavily on tags on the nodes. maybe come up with 5-20 node types that are very broad, including the names and a few key properties. Then you could allow the users to select from those base categories and create their own spin-offs by adding tags.
Say you had your basic categories of (:Thing{Name:"",Place:""}) and (:Object{Category:"",Count:4})
Your users would have a drop-down or something with "Thing" and "Object". They'd select "Thing" for instance, and type a new label (Say "Cool"), values for "Name" and "Place", and add any custom properties (IsAwesome:True).
So now you've got a new node (:Thing:Cool{Name:"Rock",Place:"Here",IsAwesome:True}) Which allows you to query by broad categories or a users created categories. Hopefully this would keep each broad category to a proportional fraction of your overall node count.
Not sure if this is exactly what you're asking for. Good luck!
Hmm. While this isn't insane, think about the type of system you're replacing first. SQL. In SQL databases you wouldn't use branches because it's data storage. If you're trying to get data from multiple sources into one DB, I'd suggest exporting them all to CSV files and using a MERGE statement in cypher to bring them all into your DB at once.
This could manifest similar to branching by having each person run a script on their own copy of the DB when you merge that takes all the nodes and edges in their copy and puts them all into a CSV. IE
MATCH (n)-[:e]-(n2)
RETURN n,e,n2
Then comparing these CSV's as you pull them into your final DB to see what's already there from the other copies.
IMPORT CSV WITH HEADERS FROM "file:\\YourFile.CSV" AS file
MERGE (N:Node{Property1:file.Property1, Property2:file.Property2})
MERGE (N2:Node{Property1:file.Property1, Property2:file.Property2})
MERGE (N)-[E:Edge]-(N2)
This will work, as long as you're using node types that you already know about and each person isn't creating new data structures that you don't know about until the merge.