I'm trying use Cypher query with Spark. I'm following this guide: https://github.com/opencypher/morpheus
but I'm not able to use path patterns p=()-[]->().
My main problem is that I want to calculate the level of relationship.
For example:
p=(u)-[:rel]-(f) return length(p)
Thank's for answer.
Related
I'm getting started with Knowledge Studio and Natural Language Understanding.
I'm able to deploy a machine-learning model toNatural Language Understanding and use the API to query it.
I would know if there's a way to deploy only the pre-annotator.
I read from Knowledge Studio's documentation that
You can deploy or export a machine-learning annotator. A dictionary pre-annotator can only be used to pre-annotate documents within Watson Knowledge Studio.
Does exist a workaround to create a model that simply does the job of the pre-annotator, i.e. use dictionaries to find entities instead of the machine-learning model?
Does exist a workaround to create a model that simply does the job of the pre-annotator, i.e. use dictionaries to find entities instead of the machine-learning model?
You may need to explain this better in what you need.
WKS allows you to pre-annotate documents with dictionaries you upload. Once you have created a ML model, you can alternatively use that to annotate your training documents, and then manually correct. As you continue the amount of manual work will reduce after each model iteration.
The assumption is that you are creating a model with a reasonable amount of examples. In your model results, you will want the mention/relations to be outside or close to outside the gray area of the report.
The other interpretation of your request I took was you want to create a dictionary based model only. This is possible using the "Rule-Based Model" functionality. You would have to create the parsing rules but you just map what you want to find to the dictionary/rule.
Using this in production though is still limited. You should get a warning when you deploy these kinds of models.
It's slightly better than just a keyword search as you can map items to parts of speech.
The last point. The purpose of WKS is to create a machine learning model which will do the work in discovering new terms you haven't seen before. With the rule based engine it can only find what you explicitly tell it to find.
If all you want is just dictionary entries, then you can create a very simple string comparison solution, but you lose the linguistic features.
According to this
Spark Catalyst is An implementation-agnostic framework for manipulating trees of relational operators and expressions.
I want to use Spark Catalyst to parse SQL DMLs and DDLs to write and generate custom Scala code for. However, it is not clear for me by reading the code if there is any wrapper class around Catalyst that I can use? The ideal wrapper would receive a sql statement and produces the equivalent Scala code. For my use case would look like this
def generate("select substring(s, 1, 3) as from t1") =
{ // custom code
return custom_scala_code_which is executable given s as List[String]
}
This is a simple example, but the idea is that I don't want to write another parser and I need to parse many SQL functionality from a legacy system that I have to write a custom Scala implementation for them.
In a more general question, with a lack of class level design documentation, how can someone learn the code base and make contributions?
Spark takes SQL queries using spark.sql. For example: you can just feed the string SELECT * FROM table as an argument to such as spark.sql("SELECT * FROM table") after having defined your dataframe as "table". To define your dataframe as "table" for use in SQL queries create a temporary view using
DataFrame.createOrReplaceTempView("table")
You can see examples here:
https://spark.apache.org/docs/2.1.0/sql-programming-guide.html#running-sql-queries-programmatically
Dataframe automatically changes into RDD and optimise the code, and this optimization is done through Catalyst. When a programmer writes a code in Dataframe , internally code will be optimized. For more detail visit
Catalyst optimisation in Spark
Summary
I am writing a gremlin script to work for both orientdb and neo4j.
For a sample, purposes let say we want to load a vertex with id 1
for neo4j, we will write the gremlin script as
g.V(1) and for orientDB g.V('#17:0').
such that my script should run for both the databases?
You can't have a vendor independent element identifier as most graph systems don't let you assign the identifier and neither Neo4j or OrientDB allow for that. You likely shouldn't be hardcoding identifiers in your code anyway as I believe that those can change out from under you depending on the graph system.
The correct approach would be to rely on indices and prefer to write your traversals as:
g.V().has('myId', 1234)
in which case any graph database could resolve that. If you do work with the native graph identifiers, I suggest you always treat them as variables in your code as in:
Object vid = g.V().has('myId', 1234).id().next()
...
g.V(vid).out().....
I'm running OrientDb 2.2.6 using a plocal connection. I'm translating from one domain specific query language to OrientDb SQL. Since I need to translate a like command that allows regular expressions, I'd like to pass a regular expression similar to
[Ss]ay.*
but it looks like OrientDb 2.2 only supports the wild card '%'. Can Orient handle a regular expression like the one above? If not, do I need to create a custom function? Maybe there's an object in Orient's API that I can use instead?
you can use something like
Select from v where name MATCHES "<regex>"
link1
link2
I'm trying to figure out what's the best solution to find all nodes of certain types around a given GPS-Location.
Let's say I want to get all cafes, pubs, restaurant and parks around a given point X.xx,Y.yy.
[out:json];(node[amenity][leisure](around:500,52.2740711,10.5222147););out;
This returns nothing because I think it searches for nodes that are both, amenity and leisure which is not possible.
[out:json];(node[amenity or leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity,leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity;leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity|leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity]|[leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity],[leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity];[leisure](around:500,52.2740711,10.5222147););out;
These solutions result in an error (400: Bad Request)
The only working solution I found is the following one which results in really long queries
[out:json];(node[amenity=cafe](around:500,52.2740711,10.5222147);node[leisure=park](around:500,52.2740711,10.5222147);node[amenity=pub](around:500,52.2740711,10.5222147);node[amenity=restaurant](around:500,52.2740711,10.5222147););out;
Isn't there an easier solution without multiple "around" statements?
EDIT:
Found This on which is a little bit shorter. But still multiple "around" statements.
[out:json];(node["leisure"~"park"](around:400,52.2784715,10.5249662);node["amenity"~"cafe|pub|restaurant"](around:400,52.2784715,10.5249662););out;
What you're probably looking for is regular expression support for keys (not only values).
Here's an example based on your query above:
[out:json];
node[~"^(amenity|leisure)$"~"."](around:500,52.2740711,10.5222147);
out;
NB: Since version 0.7.54 (released in Q1/2017) Overpass API also supports filter criteria with 'or' conditions. See this example on how to use this new (if: ) filter.