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Big Events

Big Events. Hans-Arno Jacobsen Middleware Systems Research Group MSRG.org. Big Event Data. Traditional Big Data Domain vs. Rest of Universe. There are other emerging domains with needs similar to Big Data Smart grids Smart cities ….

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Big Events

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  1. Big Events Hans-Arno Jacobsen Middleware Systems Research Group MSRG.org

  2. Big Event Data

  3. Traditional Big Data Domain vs. Rest of Universe • There are other emerging domains with needs similar to Big Data • Smart grids • Smart cities … My first message: There are other relevant Big Data domains – beware! H.-A. Jacobsen

  4. Smart Grids for Taming The Energy Problem H.-A. Jacobsen

  5. Relevance of Smart Grids • Increasing penetration of variable renewable energy sources like wind and solar et al. • Paradigm shift from demand-following supply to supply-following demand • Need fornew large-scale information system infrastructureto control demand H.-A. Jacobsen

  6. Distributed Generation, Flexible Loads and Energy Storage • Come in big numbers • Show uniquebehavior (users, weather, equipment, …) • Have to be monitored and controlled Big event data challenge H.-A. Jacobsen

  7. Solar Photovoltaic Power Generation ~2.3 TB per year and 1k panels High frequency measurements required Several metrics of interest, many spatially distributed measurement points H.-A. Jacobsen Source: National Oceanic & Atmospheric Administration (U.S.)

  8. Use of PEVs as Grid Resource ~ 0.5 TB per year and 1k vehicles • High frequency measurements required • Important for SG applications: Continuous update of trip destination and energy level at destination H.-A. Jacobsen Source: Auto21 Project, University of Winnipeg

  9. Electric Power Consumption ~ 27.5 PB per year and 1k homes • Very high frequency measurements required (e.g., for inferring device on/off events, grid stability, etc.) • Several metrics of interest (household electricity meters, single devices, etc.) Source: UCI Machine Learning Repository H.-A. Jacobsen

  10. Traditional Big Data Domain vs. Rest of Universe My second message: Detecting events in real-time in the sea of Big Data is just as important. H.-A. Jacobsen

  11. Towards Big Events • Many non-traditional scenarios that require filtering of Big Events at large scales • … scenarios that require filtering & storage of events at large scales • Filtering & storage of “event streams” • Filtering & storage of “event showers” H.-A. Jacobsen

  12. Event Showers vs. Event Streams Event Showers Event Stream Processing Linearly ordered event sequences Schema-based, single schema per stream Stream tuples follow schema More single-expression processing-based Aggregation is a key requirement Focused on processing queries/expressions over event streams • Partially ordered sets of events • No single event schema     • Events vary in shape and size from one to the next • Processing of many event expressions • Tends to require support for aggregation • Broader model & paradigm (dissemination, matching, coordination) H.-A. Jacobsen

  13. Conclusions • Big Events are Big Data in motion • Processing Big Data in real-time to detect events of interest is important as well • There are other emerging application domains; let us watch out for them My final message: Big Data Benchmarking efforts should take this into account. H.-A. Jacobsen

  14. Acknowledgements • C. Goebel for help with smart grid slides H.-A. Jacobsen

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