Apoc 1

[Asdrubal Dagreat]

APOChas been split in two parts, a Core module which contains 450+ commonly used procedures and functions (most also available on Aura).The additional extended module which contains 50 procedures that have external dependencies or are more experimental in nature (see below).

The APOC-Core library can be installed with a single click in Neo4j Desktop, can be enabled with the Docker Image, is available in all Neo4j Sandboxes and in Neo4j AuraDB and AuraDS.In a Neo4j binary download, you can find the library in the labs folder, just copy it over to the plugins folder to make all non-restricted functionality available.

APOC Extended is an open-source project, not maintained by Neo4j but the contributor community.It contains procedures and functions for data integration, exporting data, cypher based procedures, natural-language-processing (NLP) and more.

For APOC Extended download the appropriate release (same leading version numbers) for your Neo4j version into the plugins folder and restart the server.You might need to enable restricted procedures or add an extra $NEO4J_HOME/conf/apoc.conf for configuration settings.

I would like to include this URL in a Neo4j query to retrieve the data directly in Neo4j. So I turned to APOC. Below is a query that calls apoc.load.json, which you can paste into your Neo4j Desktop query window for testing:

Neo.ClientError.Procedure.ProcedureCallFailed: Failed to invoke procedure apoc.load.json: Caused by: java.lang.RuntimeException: Can't read url or key Welcome - pne data Stavangerregionen as json: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target

But I don't think that's the version used by Neo4j Desktop, which I am using here. Neo4j Desktop installs Neo4j Enterprise, and Neo4j Enterprise supplies its own JDK, which I read somewhere is Zulu OpenJDK. (I can't remember the source.)

An answer to the question in that post would help answer this one. If I can find how to swap out Neo4j Enterprise's JDK I would experiment with different JDKs and the website certificate issue of this post.

I found a work-around for using Neo4j to directly pull the data from the Open Data site, by using an alternative url. The original url I used when stating the question invokes an API provided by the organization CKAN. And that requires a certificate. But near the api link there is also a download button that fetches it in .csv format. That link does not require Neo4j to negotiate with the above API. So here is the working cypher query with the download url.

Would like to add that I am able to download the wiki.json file and import it using CALL apoc.load.json(file///) but this doesn't really fix the problem as it fails when trying to fetch a file in another Neo4J example shown here

I have a data set including multiple manually labeled images. I want to use a RandomForest classifier in Python using multiple input images to automatically label different regions of the image. From here I understood that apoc is doing exactly what I want.

The python build-in help for the train() function of the object classifier provides detailled information about which features are possible. However, the help doc for the train() function of the pixel classifier only states:

I think you only need to reformat/reshape your data and then it should work. apoc expects multiple channels (RGB, but also 2-channel or 7-channel images) passed as lists of images. See this notebook for an example:

I could imagine that this happens if the max_depth and num_ensembles parameters are too low. If the forest has just 10 trees, it might not be able to differentiate 8 labels. I just played a bit with the graphical user interface of apoc in napari, where you can easily play with these settings.

Currently I get an LogicError: clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR while trying to use the napari-accelerated-pixel-and-object-classification. However, I can use apoc from within python as described above.

If you build such complicated features, I would wonder how you come up with this combination. Is there any literature where such filters are used for similar purposes? Furthermore, consider passing a list of images instead of a string that generate a list of images. It may be more convenient to handle. In this way you can also feed in feature images that have been computed with other libraries such as scikit-image.

Neo4j 3.2 introduces user defined aggregation functions, we will use that feature in APOC in the future, e.g. for export, graph-algorithms and more, instead of passing in Cypher statements to procedures.

Please note that about 70 procedures have been turned from procedures into user defined functions.This includes, apoc.date.* apoc.number.*, apoc.coll.*, apoc.map.* and some more packages.See, this issue for a list.

If you used or wrote procedures in the past, you most probably came across instances where it felt quite unwieldy to call a procedure just to compute something, convert a value or provide a boolean decision.

Really useful to quickly find a subset of relationships between nodes with many relationships (tens of thousands to millions) is apoc.index.between.Here you bind both the start and end-node and provide (or not) properties of the relationships.

Indexes are used for finding nodes in the graph that further operations can then continue from.Just like in a book where you look at the index to find a section that interest you, and then start reading from there.A full text index allows you to find occurrences of individual words or phrases across all attributes.

Things start to get interesting when we look at how the different entities in Paris are connected to one another.We can do that by finding all the entities with addresses in Paris, then creating all pairs of such entities and finding the shortest path between each such pair:

apoc.index.addAllNodes(, , ) allows to fine tune your indexes using the options parameter defaulting to an empty map.All standard options for Neo4j manual indexes are allowed plus apoc specific options:

As mentioned above, apoc.index.addAllNodes() populates an fulltext index.But it does not track changes being made to the graph and reflect these changes to the index.You would have to rebuild that index regularly yourself.

In addition to enable index tracking globally using apoc.autoIndex.enabled each individual index must be configured as "trackable" by setting autoUpdate:true in the options when initially creating an index:

By default index tracking is done synchronously.That means updates to fulltext indexes are part of same transaction as the originating change (e.g. changing a node property).While this guarantees instant consistency it has an impact on performance.

The values above are the default setting.In this example the index updates are consumed in transactions of maximum 50000 operations or 5000 milliseconds - whichever triggers first will cause the index update transaction to be committed and rolled over.

The phonetic text (soundex) procedures allow you to compute the soundex encoding of a given string.There is also a procedure to compare how similar two strings sound under the soundex algorithm.All soundex procedures by default assume the used language is US English.

The User Function apoc.data.domain will take a url or email address and try to determine the domain name.This can be useful to make easier correlations and equality tests between differently formatted email addresses, and between urls to the same domains but specifying different locations.

The prec parameter let us to set the precision of the operation result.The default value is 0 (unlimited precision arithmetic) while for 'roundingModel' the default value is HALF_UP. For other information abouth prec and roundingModel see the documentation of MathContext

Weights are computed by multiplying the relationship weight with the weight of the other nodes.Both weights are taken from the 'weight' property; if no such property is found, the weight is assumed to be 1.0.Similarly, if no 'weight' property key was specified, all weights are assumed to be 1.0.

whitelist filter - All nodes in the path must have a label in the whitelist (exempting termination and end nodes, if using those filters).If no whitelist operator is present, all labels are considered whitelisted.

termination filter - Only return paths up to a node of the given labels, and stop further expansion beyond it.Termination nodes do not have to respect the whitelist. Termination filtering takes precedence over end node filtering.

end node filter - Only return paths up to a node of the given labels, but continue expansion to match on end nodes beyond it.End nodes do not have to respect the whitelist to be returned, but expansion beyond them is only allowed if the node has a label in the whitelist.

The label is additionally whitelisted, so expansion will always continue beyond an end node (unless prevented by the blacklist).Previously, expansion would only continue if allowed by the whitelist and not disallowed by the blacklist.This also applies at a depth below minLevel, allowing expansion to continue.

When at depth below minLevel, expansion is allowed to continue and no pruning will take place (unless prevented by the blacklist).Previously, expansion would only continue if allowed by the whitelist and not disallowed by the blacklist.

The one returned path only matches up to 'Gene Hackman'.While there is a path from 'Keanu Reeves' to 'Clint Eastwood' through 'Gene Hackman', no further expansion is permitted through a node in the termination filter.

When processing the labelFilter string, once a filter operator is introduced, it remains the active filter until another filter supplants it.(Not applicable after February 2018 release, as no filter will now mean the label is whitelisted).

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