Synerise Monad - Real-Time Multimodal Behavioral Modeling
Jacek Dąbrowski, Barbara Rychalska · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
The growth of time-sensitive heterogeneous data in industry-grade datalakes has recently reached unprecedented momentum. In response to this, we propose Synerise Monad - a prototype of a real-time behavioral modeling platform for event-based data streams. It automates representation learning and model training on massive data sources with arbitrary data structures. With Monad we showcase how to automatically process various data modalities, such as temporal, graph, categorical, decimal, and textual data types, in a time-sensitive way allowing for real-time time feature creation and predictions. Monad's distributed and scalable architecture coupled with efficient award-winning algorithms developed at Synerise - Cleora and EMDE - allows to process real-life datasets composed of billions of events in record time. The Monad ecosystem showcases a viable path towards real-time event-based AutoML.