Client had:

Traditional approaches to anomaly detection in grid networks are operator based in a grid opartaing centre. Operators use collected near real time metrics from the grid to asses and antipicate anamolies. The Operator must decide based on experience which mitigating measure to use and lacks the ability to simulate scenarios in real time.

We did:

Hivemind implemented a near real time anamoly solution based on Apache Kafka and Apache Spark, processing 3,6 m data points per sec to determine anamolies in the current and voltage data porints and fingerprint anamalous data sets for simualtion of a downstream proedictive systems.

Client had:

Due to the success of their CBIS platform, management wanted to see new reporting and API's. Therefore, the platform must now be always available and reliable to serve these new requests. Anomalies and problems from the source systems must be identified quickly and data must be up-to-date at all times.

We did:

Hivemind has supported PAYONE in the design, operation and implementation of the CBIS platform. Its goal is to unify the different source systems they have gathered through various mergers into a uniform reporting tool with a central data model. To achieve this Hivemind has introduced architectures for near real time reporting and streaming as well as cloud services. By using elastic and scalable services in public clouds we have also implemented cost reduction solutions. In addition, the PAYONE team has been trained on the new architecture and technologies in order to independently maintain the platform.