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Defining window operations in RML #85
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@s-minoo is this a specific request for join conditions? If this is the case, please confirm me so I can move it to the proper repository |
It is indeed a specific request for joins. So, I think it's more relevant to rml-join repo. |
Transfer to its corresponding repository then |
The rml-jc repo will be closed. Moving this unsolved issue back to rml-core. |
I would suggest to leave this issue for the working-group |
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Issue
Currently, there is no way to define windowing semantics in RML.
Windowing is crucial when evaluating joins between different live streaming
data sources.
Furthermore, windowing could also support buffering capabilities for
aggregation functions when processing streaming data sources. For example,
calculating an average of the values over the last 5 minutes.
Requirements
According to Gedik B.,
windows' behaviour is defined based on its type, and policies.
There are 2 main types of windows: tumbling, and sliding windows.
An illustration about how these windows work can be found here.
Note: Session window is a special case of tumbling window where the window
only gets dropped when inactivity threshold is violated.
The policies control when the windows evicts the tuples inside
the window (eviction policy), and when they triggers the processing of the
tuples using the operator logic defined inside the window (trigger policy).
Policies are further divided into 4 categories namely:
inform when to evict/trigger. E.g. When the temperature value of a sensor is above 40C.
when to evict/trigger.
Thus, we need a set of vocabulary to define and configure windows by
describing:
The true semantics and combination of the policies are further explained by
Gedik B..
Example
Given the following RML with a join condition:
Windows could be defined in the object map
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