Scalable technology for monitoring of IT networks with embedded Artificial Intelligence and Machine Learning, aiming an early warning and risk prevention

PROGRAMISTA AD is a software developer awarded by BASSCOM as the best software company for 2016, and the second best employer in the IT industry for 2016. The company is specialized in software development, mainly in partnership with leading international software companies. The project supports the development of a process innovation–scalable technology for monitoring of IT networks with embedded Artificial Intelligence and Machine Learning, aiming an early warning and risk prevention. The technology is a cluster group of highly scalable software mechanisms and tools, designed to perform efficient monitoring of computer networks of enterprises in big organizational structures, that is, networks including from 100 000 to over a million hardware devices. An important part of the cluster group are the embedded Artificial Intelligence and Machine Learning modules whose cores consists of specific mathematical algorithms with software implementation. The concept of the technology has been developed entirely based on the analysis of the available technologies worldwide for implementing constant monitoring over dataflow of information arrays, that are proceeded, extracted or intercepted in real time from a large number of software and hardware sources. The project implementation will result in the development of a technology falling within the scope of 2 priority axes of the thematic priorities of ISSS ‘ICT and Informatics’. Key priority axis: -Big Data, Grid and Cloud Technologies Secondary priority axis: -web, hybrid and "native" applications, web-based applications for developing and operating new services and products The innovation is a web-based cloud solution offered as a tool/technology for efficient monitoring of software-hardware networks. The project implementation will lead to the development of an innovation corresponding to TRL 6-the technology will be tested in the medium of a potential client, and the detected errors will be corrected.

Comments