Tutorial: Elastic and Fault Tolerant Event Stream Processing Using StreamMine3G
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
The massive amount of new data being generated
each day by data sources such as smartphones and sensor
devices calls for new techniques to process such continues
streams of data. Event Stream Processing (ESP) addresses this
problem and enables users to process such data streams in (soft)
realtime allowing the detection as well as a quick reaction to
relevant situations.
In this tutorial, we will introduce the participants to ESP
techniques as well as ESP systems such as Storm, Apache S4
and StreamMine3G. We will cover aspects such as program-
ming models, fault tolerance as well as elasticity and cloud
support of these platforms.
each day by data sources such as smartphones and sensor
devices calls for new techniques to process such continues
streams of data. Event Stream Processing (ESP) addresses this
problem and enables users to process such data streams in (soft)
realtime allowing the detection as well as a quick reaction to
relevant situations.
In this tutorial, we will introduce the participants to ESP
techniques as well as ESP systems such as Storm, Apache S4
and StreamMine3G. We will cover aspects such as program-
ming models, fault tolerance as well as elasticity and cloud
support of these platforms.
Details
Original language | English |
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Number of pages | 2 |
Publication status | Published - 2013 |
Peer-reviewed | Yes |
Workshop
Title | Workshop on Distributed Cloud Computing (DCC 2013) (UCC '13), IEEE Computer Society, 2013 |
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Conference number | |
Duration | 9 December 2013 |
Location | |
City | Dresden |
Country | Germany |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Keywords
- event stream processing, cep, fault tolerance, mapreduce, deterministic execution, elasticity, esp, cep, fault tolerance, mapreduce, deterministic execution, elasticity