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    Network Data Model
    Integration Modes
    NQL Tutorial
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Integration modes
Neuro4j storages are very flexible and it's possible lots of integration variations. Some of them are:
In-memory. It can be with or without persistence. In-Memory storage is a Java component which can be easy used with any other java code. in-memory model

Client - Server.Neuro4J Storages can be run as dedicated instances on the same or remote machine. client-server model
Distributed. Using Neuro4J Network Management System Storages can be distributed across network. in-memory model
Storage's Persistence implementations
Neuro4J Storages support Network Query Language (NQL) for various persistence implementations.

In-Memory Storage. This implementation allows to perform network based computing in memory. Actually it isn't persistent.

XML based Storage. It stores networks in XML files. XML Storage is the best for static workflow systems.

Lucene based Storage. Uses Apache Lucene as persistence. Works fast with large data.

Solr based Storage. Uses Apache Solr as persistence. Similar to Lucene + some Solr specific features. The best for huge data meaning and analysis.

RDBMS based Storage. Uses Relational Databases as persistence (any JDBC compliant database). Supports ACID transactions.

Multicore Storage. Works as dedicated server. Allows to manage multiple instances of different storages listed above. Has admin web based interface - NMS Console which allows to show configurations and run NQL queries.

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