We at Profium are witnessing a trend to migrate applications and services from public clouds to private clouds. There seems to be two major factors behind this trend: costs of public clouds are not cheaper than running your own IT infrastructure in some cases and security controls are not to the level of compliance required […]
Given the interest to use AI tools to provide users with recommendations or to allow decisions made based on suggestions by AI tools, companies may have to comply with regulations to explain why, for example, an application was or was not approved. Such an application might impact someone’s credit rating or claim application approval. Profium’s […]
The EU’s directive on security of network and information systems (NIS directive) is replaced with a new NIS2 directive (revised Cybersecurity Directive). The new directive defines more precise requirements, procedural instructions and reporting obligations than before to ensure a high level of cyber security and to improve the ability to tolerance disturbances throughout the EU. […]
You Are Using Graph Databases Every Day! How’s it possible that LinkedIn can show all your 1st, 2nd, and 3rd -degree connections, and the mutual contacts with your 2nd level contacts in real-time. The answer is: because LinkedIn organizes its entire contact network of 660+ million users with a graph! Did you know that also […]
Having used and deployed Kafka in conjunction with our Profium Sense product, it’s time to refill my coffee cup and reflect what’s missing in Kafka. We at Profium have enjoyed the performance and fault-tolerance to start with, we’ve appreciated the durability of messages and the connectivity to various programming language environments. What we first thought […]
Profium Sense makes it easy to quickly start working with any arbitrary data in the open RDF format. This video gives a brief demonstration of how to import any RDF data to the Profium Sense graph database using the simple web interface, using some geographic data about Ireland from geonames.org as an example. After importing […]
Background to our work lies in a rule engine that we have developed at Profium. The rules we support are in the expressive power of Datalog. This means new facts can be inferred from existing facts. The goal of this article is to initiate discussion about possible application areas for an algorithm we have developed.
The Coalition Against Insurance Fraud estimate that frauds costs $80 billion a year across all lines of insurance. In a highly competitive industry reducing such a cost is an obvious path to improving profitability. Any reduction in fraud will directly impact an insurance company’s bottom line.
Companies have invested into IT to streamline their claims processing. Sometimes an automated claim should merit human analysis. Intelligent Fraud Detection is a solution that helps you determine the conditions or rules that stop claims that may be grounded on false data or where the data is correct but the underlying elements of the claim are not justified. Profium Sense for Intelligent Fraud Detection is an ideal tool for this purpose. Data used by the rules to detect fraud may come from a diverse range of sources such as photographic metadata, geographical data and social media. With Profium solution your analysts can develop rules which take different fraud scenarios into account and save those rules to test and production environments without delays from IT.
Job portals have become a popular place for corporations to place their ads for new vacancies in the hope that skilled individuals would find them and apply for them. Often such portals provide traditional search interfaces based on forms. Both applicants and employees will then have to try several values for fields such as title to see if there are any interesting results that match their criteria. This traditional search can be greatly improved with semantic search. A new growing trend for semantic employment matching is skill based matching.
In the era of ‘big data’ software systems are commonly dealing with large amounts of complex data which may be generated by a diverse range of sources. Conventional relational databases require specifying the structure of the data up-front, and the complexity of the database design rapidly increases with the complexity of the relationships between concepts in the data. Alternatively, a graph database can come closer to modeling the real-life relationships between data and represent data in a structure that more closely models real world relationships between concepts. Profium Sense, a NoSQL in-memory graph database and Rule Engine, processes real-time flows of data, and offers rule-based inferencing and Semantic AI for real time content distribution.