Enriched Data à la BaseN: From Deluges of Data to Critical Signal Identification

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BaseN will add new data enrichment features to its Platform, enabling identifying critical signals coming from deluges of data. This is crucial for e.g. telecommunications and smart grid networks, where even a medium-sized network can have hundreds of thousands of measurement targets producing fault and performance information. 

The new features were developed during Eureka Eurostars funded project DYNE, where BaseN joined forces with the University of Applied Sciences and Arts Northwestern Switzerland FHNW. BaseN is known for its highly scalable and inherently distributed Platform, whereas FHNW has a well-established expertise in semantic technologies, encoding meaning separately from data and content files, and dynamic event analysis.  

Their collaboration in DYNE project has resulted in designing a context-aware event analysis system, which further extends BaseN’s alerting capabilities to developing more medium-term analytics, in addition to existing short-term alerting and long-term reporting functionality. Improved ways of visualising multiple parallel measurements were also developed, enabling higher level situational views and offering customers the capability to see not only single faults, but aggregated information and analysis of root causes of multiple dispersed problems. 

The Platform also provides preventive fault management, which analyses measurements, as well as their trends and changes in behaviour. By considering the overall state of all measurements and identifying the important hierarchies, the system can point the user directly to the ultimate causes of problems and help them ignore lower level simple events that arise as by-products.

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