Neo4J is a graph database, which helps relate the multidimensional data and converts the data into a knowledge base. It is an open-source, NoSQL, with native graph database platform to leverage the data relationships along with data. Neo4j is ACID-compliant transactional database with native graph storage and processing.
The traditional databases have an approach of rows, columns in a table, whereas Neo4j is structured with relationships between data and records. Other databases compute relationships at query time through expensive JOIN operations, a graph database stores connections alongside the data in the model.
The graph database is advantageous when the data topology is complex and evolves with the business, where relationships in data contribute more meaning and value.
The graph-optimized query language recognizes these stored relationships known by which is called Cypher, these Cypher queries are easier to write when compared to the complex SQL Joins to draw insights & patterns within data.
Neo4j efficiently stores, handles, and query highly-connected data in the data model, this graph data models provide options to solve many different kinds of business and technical needs that are beyond what traditional databases can do.
There are many ways to connect and interact with Neo4j and leverage its advantages of graph data, from our graph database being a core product connected to visualization tools like Bloom and Tableau, this provided the doors open with opportunities to display the solved problems to maximize business and ease the data. The graph model is unique with its ability to accommodate highly connected, partially structured datasets.
Neo4j for Graph Data Science plays a vital role in harnessing the predictive power of relationships, increasing the prediction accuracy by leveraging the graph advantages. The Graph Data Science Library is the core of this framework consisting of data science algorithms to prepare models and compute results over the nodes of data.
At SpringML we have some of the solutions for various retail problems and have successfully built the use case which shows how this database is capable of solving cases with a combination of knowledge management, informatics, and data science. This demonstration is also integrated with the data-driven visualization. We have used Neo4J as a graph database and have integrated that with Tableau.
Some of the other areas/use case graph database helps are –
– Military Logistical challenge
- Forecast for replacement parts
- Calculate mean-time to failure value
- Multidimensional cost analysis
- Comparison and trend analysis
– Budget requirement process
- It helps in answering what-if scenarios of military deployment cost and supporting equipment needs
– Network and Database infrastructure monitoring and creating knowledge graph
– Fraud detection Analytics
– Cybersecurity threat analytics
– Connecting Silos to Fight Diabetes
– Supply chain management
- Shift in consumer buying patterns
- Unpredictable costs for materials, warehouse and freights
- Changes and compliance, regulations and standards
– Real-time recommendation engine
– Master data management and more..
