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Neo4j

Graph databases are made up along a concept different to the relational ones. Graph databases store the information in the nodes (counterpart of RDBMS „record”) and by means of relations (counterpart of RDBMS „connections”). Each node and each relations can have the associated attributes (counterpart of RDBMS „column”), but the list of attributes may differ even between nodes and relations of the same type.

Being a partner of Neo4j brand, we can help with the licence purchase and / or access to the database. Additionally, we can perform the activities listed below:

  • installation of the base
  • feeding the base from external sources
  • creating the interface for online feeding the base
  • creating suitable relations
  • implementing the rules, which make specific features, labels, connections and nodes
  • preparing the reports in the Neo4j environment as well as the dedicated reporting application
  • creating other analytical-reporting environments, based on the Neo4j features
  • service and maintenance of our products created for you, as well as service & maintenance of the database itself

Main features

Graph databases are made up along a concept different to the relational ones. Graph databases store the information in the nodes (counterpart of RDBMS „record”) and by means of relations (counterpart of RDBMS „connections”). Each node and each relations can have the associated attributes (counterpart of RDBMS „column”), but the list of attributes may differ even between nodes and relations of the same type.

Main features of the graph databases, crucial for their users, are:

Efficiency

With the inqueries to involve numerous tables (diagrams), construction of the graph database allows for reaching the efficiency a few rows higher that in case of the relational databases. It happens in spite of lack of the dedicated indexes!

Quick development

Without the pre-defined data model we create data models with simultaneous data income. Intuitive „reading” the data within a model, both by IT specialists, as well as by business personnel.

Business responsiveness

Lack of the pre-defined scheme results quick reactions to the need of changes in the application (CR). Data in the graph database may be shaped along with the requirements.

From laptop to clusters

Graph databases are characterised by high accessibility, transactionality and scalability. Billions of nodes and relations can be stored within them, they can also be used locally on a laptop or desktop computer (working station), as well as a mass data stores (warehouses) in big corporations.

Usable by IT and business

Thanks to the tools built into the database, extraction and analysis of the data can be performed also by persons without the specialistic IT knowledge.

Connections between business entities

Defining the condition and the number of relations and distances between objects (e.g., we look for the shortest path between user „Mr Smith” and the „Alpha” company, with maximum distance of 5 relations), we can create a clear graph, containing nodes and relations within previously indicated transactions and conditions, and subject them to further analyses.

Additional connections between persons and companies

We can introduce to the graphs persons and relations not resulting directly from the court registers, e.g. kinship, neighbourhood, close co-operation of the companies not related formally, transactions within the supply chains etc. With further analysis we can use these relations or we can leave them out. Making additional relations can be done by defining the rules for data in the base and their implementation. These data may come from the internal base of a bank or from external sources.

Usage of the Neo4j library and function of base

Neo4j base has over 500 functions implemented. Additionally, in the APOC library, next 600 functions are available too.

Thanks to the structure of the graph databases (Neo4j Graph Data Science Library) and internally implemented mechanisms, we can:

  • identify seemingly disconnected groups, which use the same identifiers (Louvain Modularity algorithm)
  • identify groups often being in touch with each other (e.g. during banking operations; Components Union Find algorithm)
  • check the similarity of the bank accounts or similarity of the connection chains (Jaccard algorithm)
  • check the influence upon other persons or entities and the amount of the transactions between them (PageRank algorithm)
  • find additional relations and add them to already existing data (e.g. neighbourhood, the same IP addresses; Common Neighbors algorithm)
  • find the transactions or connections of a very short path (Shortest Path algorithm)

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