Jacek Dąbrowski
ABSTRACT
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.
- Michaŀ Daniluk, Jacek Dąbrowski, Barbara Rychalska, and Konrad Goŀuchowski. 2021. Synerise at KDD Cup 2021: Node Classification in Massive Heterogeneous Graphs. Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) KDD Cup Open Graph Benchmark (OGB) Challenge (2021). https://ogb.stanford.edu/paper/kddcup2021/mag240m_SyneriseAI.pdfGoogle Scholar
- Michaŀ Daniluk, Barbara Rychalska, Konrad Goŀuchowski, and Jacek Dąbrowski. 2021. Modeling Multi-Destination Trips with Sketch-Based Model. 14th ACM International Web Search and Data Mining Conference (WSDM) WebTour Workshop on Web Tourism (2021). http://ceur-ws.org/Vol-2855/challenge_short_3.pdfGoogle Scholar
- Jacek Dąbrowski, Barbara Rychalska, Michaŀ Daniluk, Dominika Basaj, Konrad Goŀuchowski, Piotr Bąbel, Andrzej Michaŀowski, and Adam Jakubowski. 2021. An Efficient Manifold Density Estimator for All Recommendation Systems. In Neural Information Processing: 28th International Conference, ICONIP 2021.Google Scholar
- Barbara Rychalska, Piotr Bąbel, Konrad Goŀuchowski, Andrzej Michaŀowski, Jacek Dąbrowski, and Przemysŀaw Biecek. 2021. Cleora: A Simple, Strong and Scalable Graph Embedding Scheme. In Neural Information Processing: 28th International Conference, ICONIP 2021.Google Scholar
- Barbara Rychalska and Jacek Dabrowski. 2020. Synerise at SIGIR Rakuten Data Challenge 2020: Efficient Manifold Density Estimator for Cross-Modal Retrieval. The 43th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) eCom Workshop Challenge (2020). https://sigirecom.github.io/ecom20DCPapers/SIGIR_eCom20_DC_paper_1.pdfGoogle Scholar
Index Terms
- Synerise Monad - Real-Time Multimodal Behavioral Modeling
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