STREAMLINE is an EU funded Research and Innovation project.


Today’s big data analytics systems cater to either “data at rest” or “data in motion.” As a result, enterprises are left to devise costly strategies to support and integrate disparate systems. To alleviate this burden, STREAMLINE aims to reduce complexity, enable faster results, and support both “data at rest” and “data in motion” in a single system. STREAMLINE research and innovation actions include carrying out groundbreaking research in the area of distributed systems, data management, and machine learning with the key goal to arrive at sustainable innovation in the area of fast and big data analysis. STREAMLINE will achieve sustainability by building upon and feeding back into Apache Flink, a leading open-source data analysis system that empowers European enterprises to jointly carry out data analysis for both “data at rest” and “data in motion” far more efficiently and for much lower cost than with existing tools and technologies. Furthermore, STREAMLINE will demonstrate impact in four reactive and proactive analytics applications: one focused on customer retention, another on personalised recommendation, the third on targeted advertisement and the fourth on multilingual Web processing.

STREAMLINE innovation objectives address the lack of established technologies for high accuracy and reactivity predictive methods that are easy to develop, maintain and operate. Our vision manifests in the magic triangle (above) with the dimensions of skills shortage, delayed information processing, and lack of appropriate analytics. The vision of STREAMLINE is to improve on all of these dimensions, by streamlining data analysis, by reducing the software complexity by introducing a novel architecture and programming paradigm, and by reducing the time it takes to arrive at actionable intelligence via the introduction of innovative approximate methods and interactivity.  We will reduce the skill requirements through a declarative language, which alleviates the burden data scientists face today, i.e., the need to know & operate two different systems and their respective tuning parameters. Achieving this would enable more people to easily create data analysis programs for ever-faster and increasingly bigger datasets. This effectively means that STREAMLINE reduces cost, improves performance, and creates new functionality for data analysis & related business applications



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