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We present a system for online probabilistic event forecasting. We assume that a user is interested in detecting and forecasting event patterns, given in the form of regular expressions. Our system can consume streams of events and forecast when the pattern is expected to be fully matched. As more events are consumed, the system revises its forecasts to reflect possible changes in the state of the pattern. The framework of Pattern Markov Chains is used in order to learn a probabilistic model for the pattern, with which forecasts with guaranteed precision may be produced, in the form of intervals within which a full match is expected. Experimental results from real-world datasets are shown and the quality of the produced forecasts is explored, using both precision scores and two other metrics: spread, which refers to the “focusing resolution” of a forecast (interval length), and distance, which captures how early a forecast is reported.

Fri 23 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

10:30 - 12:10
Session 5: Learning, Automation and IntegrationDEBS Research Papers at Sala d'Actes, Vertex Building
Chair(s): Martin Hirzel IBM Research
10:30
25m
Talk
Event Forecasting with Pattern Markov Chains. (Research Paper)
DEBS Research Papers
Elias Alevizos NCSR Demokritos, Institute of Informatics and Telecommunications, Alexander Artikis University of Pireaus and NCSR "Demokritos", Georgios Paliouras Institute of Informatics & Telecommunications, NCSR "Demokritos"
10:55
25m
Talk
Automatic Learning of Predictive CEP Rules: Bridging the Gap between Data Mining and Complex Event Processing. (Research Paper)
DEBS Research Papers
Raef Mousheimish DAVID lab, University of Versailles, Yehia Taher DAVID - UVSQ, Karine Zeitouni University of Versailles-Saint-Quentin
11:20
20m
Talk
An Event-based Capture-and-Compare Approach to Support the Evolution of Systems of Systems. (Experience Paper)
DEBS Research Papers
Jürgen Thanhofer-Pilisch Christian Doppler Lab. MEVSS, Johannes Kepler University Linz, Rick Rabiser Christian Doppler Lab. MEVSS, Johannes Kepler University Linz, Thomas Krismayer Christian Doppler Lab. MEVSS, Johannes Kepler University Linz, Michael Vierhauser University of Notre Dame, Paul Grünbacher , Stefan Wallner Primetals Technologies Austria GmbH, Klaus Seyerlehner Primetals Technologies Austria GmbH, Helmut Zeisel Primetals Technologies Austria GmbH
11:40
20m
Talk
Using Rank Aggregation in Continuously Answering SPARQL Queries on Streaming and Quasi-static Linked Data. (Experience Paper)
DEBS Research Papers
Shima Zahmatkesh Politecnico di Milano, Emanuele Della Valle DEIB, Politecnico di Milano, Daniele Dell'Aglio IFI, University of Zurich