Profium Sense™ Graph Database

Graph database market is expected to grow from USD 1.9 billion in 2021 to USD 5.1 billion by 2026. Applications like digital asset or logistics or collection management  require flexible ways of storing relationships between objects for real-time retrieval of complex data. Unlike relational databases and graph databases built on relational databases, native graph databases like Profium Sense™ Graph Database can store complex interconnected data and enable versatile queries traversing through the data with low latency.

Profium Sense™ Graph Database is a native in-memory RDF graph database that has a patented Rule Engine optimised for real-time operations.

Profium Sense™ Graph Database can constantly monitor and ingest data from heterogeneous sources such as news feeds, CRM, IoT, Open and Big Data. The data is then enriched and interlinked with the help of Profium’s patented rule engine. This approach gives users real-time access to the data and allows them to make smarter decisions and perform their daily work more efficiently.

Patented Rule Engine

Profium Sense Rule Engine is a proven Artificial Intelligence (AI) engine to evaluate logical rules. It is based on a patented algorithm that is capable of inferencing over high throughput of concurrent inserts and delete operations in parallel. Profium Sense capabilities include reasoning over RDF metadata using configurable and dynamically modifiable rule sets and ontologies.

Efficient rule management with Profium Sense™ Studio

Profium Sense™ Studio is an easy-to-use graphical rule editor for defining classification rules. Using the editor user can create complex rules that can share common parts. Once the rule definition is complete, users can saves it with a click of a button to Profium Sense™ Graph Database.

Incremental inference: no need to recalculate over large datasets

Inference takes place incrementally and the algorithm is fully bidirectional: frequent updates and rule set changes will not require complete recalculation over large datasets. This unique approach makes it ideal for gigabyte size datasets with frequent updates. Reasoning algorithm takes place as forward chaining inference where all inferred metadata is materialized during insertion time, making no performance implications to information retrieval or query processing.

RDFS, OWL and custom rules supported

Rule Engine supports custom rules as well as ontologies such as RDF Schema (RDFS) and Web Ontology Language (OWL) . Using these ontologies helps enrich your content descriptions automatically.

Expression power of custom rules include not only trivial data transformations and combinations, but also complex filtering using built-in comparison operators for numerics and dates, geographical and distance matching, text and regular expression matching and user-defined functions using JavaScript.

Lightning fast queries through in-memory architecture

Profium Sense™ Graph Database stores triples in memory, which provides for low latency queries on complex data sets. Optimised full text indexing enables efficient searches for textual content which is encoded in graph

Product features

Database model RDF store
Current release 7
License commercial
Deployment models Cloud and on-prem
Implementation language Java
Server operating systems Linux (RHEL 8)
Data scheme schema-free
Typing Yes
XML support Yes
Access SPARQL, Java API, HTTP API, Groovy
Supported programming languages Java
Triggers Yes
Replication methods HA-Cluster
Consistency concept Eventual consistency
Transaction concepts ACID (single node)
Inferencing Forward chaining (Horn clauses)
Concurrency Yes
Persisting data Yes

Profium products run on Profium Sense™ Graph Database

Profium’s products (Profium Sense™ DAM, Profium Sense Collection management) as well as solutions (Profium Sense™ Contract archive) are based on the Profium Sense™ Graph Database. However, it can also be used as a base for customised applications like in the case of AFP .

Profium Sense™ Graph database application project

Profium Sense™ Graph Database can be customised to solve customer specific business problems by

  • customising the ingestion and access
  • creating rules for the rule engine
  • implementing custom logic

Our approach for custom project has two phases:

  1. Proof-of-concept phase
  2. Solution creation phase

Target of the first phase is to capture and analyse business requirements and turn them using a iterative process in a prototype with which the customer can validate the viability of the solution and make a go/no-go decision for the solution creation phase.

Second phase target is to create and deploy the first version of the solution based on the experience from the proof of concept use. Once the solution is in use requirements are captured and the roadmap for incremental development is updated accordingly.