Process Continuous Streams of Large Volumes of Data to Detect Conditions and Anomalies in an Instant
Session Date: Mar 05, 2020
Time: 7:00am (PT) | 3:00pm (GMT)
Stream processing unifies applications and analytics by processing data as it arrives, in real-time, and detects conditions within a short period of time from when data is received. The key strength of stream processing is that it can provide insights faster, often within milliseconds to seconds. With that being said, stream processing naturally fits with time series data, as most continuous data series are time series data.
In this webinar, Riccardo will discuss how stream processing, from an information-need perspective, requires considering order, context and responsiveness. For these three characteristics, state-of-the-art stream processing provides the necessary theoretical foundations. But a language that reaches the right level of expressiveness is needed. This is where Flux, InfluxDB’s scripting and query language, comes in.
Riccardo Tommasini
Assistant Professor, University of Tartu
Riccardo Tommasini is an Assistant Professor at the University of Tartu, Estonia. Riccardo did his PhD at the Department of Electronics and Information of the Politecnico di Milano with a thesis on "Velocity on the Web". The thesis investigates the velocity aspects that concern the Web environment, together with other challenges such as variety and volume. His research interests span Stream Processing, Knowledge Graphs, Logics and Programming Languages. Riccardo's tutorial activities comprise Stream Reasoning Tutorials at ISWC 2017, ICWE 2018, ESWC 2019, and TheWebConf 2019, and DEBS 2019, IEEE Big Data 2021.
