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Big Events. Hans-Arno Jacobsen Middleware Systems Research Group 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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Big events

Big Events

Hans-Arno Jacobsen

Middleware Systems Research Group

Big event data

Big Event Data

Traditional big data domain vs rest of universe

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 –


H.-A. Jacobsen

Smart grids for taming the energy problem

Smart Grids for Taming The Energy Problem

H.-A. Jacobsen

Relevance of smart grids

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

Distributed generation flexible loads and energy storage

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

Solar photovoltaic power generation

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.)

Use of pevs as grid resource

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

Electric power consumption

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

Traditional big data domain vs rest of universe1

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

Towards big events

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

Event showers vs event streams

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



  • 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



  • C. Goebel for help with smart grid slides

H.-A. Jacobsen

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