Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production Platform

Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.

Nikos Giatrakos is an Assistant Professor at the School of ECE, Technical University of Crete. His research focuses on software architectures, algorithms and systems for Big Data Management including Big streaming Data, Decentralized Big Data Processing, Stream Summarization and Complex Event Processing.

Elias Alevizos is a post-doctoral researcher at NCSR Demokritos. His research interests lie in the fields of Data Science, Artificial Intelligence and Complex Event Recognition/Forecasting.

Antonios Deligiannakis is a Professor at the School of ECE, Technical University of Crete. His research interests are in the area of Big Data Analytics over data streams, including distributed data processing, complex event processing in large scale systems, approximate query processing and large scale data mining.

Ralf Klinkenberg, founder and head of research at RapidMiner and senior director of data science research at Altair, is a data-driven entrepreneur with more than 30 years of experience in ML, AI and advanced data analytics research, software development and consulting. In 2008 he won the European Open Source Business Award and in 2016 the European Data Innovator Award. Today, RapidMiner has more than 1 Mio. registered users in more than 150 countries world-wide and is one of the most widely used predictive analytics platforms.

Alexander Artikis is an Associate Professor at the University of Piraeus, and a Research Associate at NCSR Demokritos, leading the Complex Event Recognition group. He has more than 100 publications in the fields of Artificial Intelligence and Data Science. According to Google Scholar his h-index is 36. Alexander has given tutorials in various conferences, such as VLDB, IJCAI and KR, and has co-organised the Dagstuhl seminar on the Foundations of Composite Event Recognition.

• Synopses Data Engine-as-a-Service Component
• CEF Component: Wayeb
• Optimizer Component
• RapidMiner Streaming Extension

Besides the source code of RapidMiner Studio Streaming Extension provided per the above link, the Streaming Extension can be installed (having downloaded RapidMiner Studio (version 9.10)) from RapidMiner Marketplace .

© Nikos Giatrakos, Elias Alevizos, Antonios Deligiannakis, Ralf Klinkenberg, Alexander Artikis 2023