[jira] [Commented] (SPARK-4727) Add "dimensional" RDDs (time series, spatial)

["David Sabater (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4727?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14609095#comment-14609095 ] 

David Sabater commented on SPARK-4727:
--------------------------------------

[~sandyr] is actually working on something similar:
https://github.com/cloudera/spark-timeseries

I am actually interested in ARIMA models which needs timeSeriesRDD to be implemented.

> Add "dimensional" RDDs (time series, spatial)
> ---------------------------------------------
>
>                 Key: SPARK-4727
>                 URL: https://issues.apache.org/jira/browse/SPARK-4727
>             Project: Spark
>          Issue Type: Brainstorming
>          Components: Spark Core
>    Affects Versions: 1.1.0
>            Reporter: RJ Nowling
>
> Certain types of data (times series, spatial) can benefit from specialized RDDs.  I'd like to open a discussion about this.
> For example, time series data should be ordered by time and would benefit from operations like:
> * Subsampling (taking every n data points)
> * Signal processing (correlations, FFTs, filtering)
> * Windowing functions
> Spatial data benefits from ordering and partitioning along a 2D or 3D grid.  For example, path finding algorithms can optimized by only comparing points within a set distance, which can be computed more efficiently by partitioning data into a grid.
> Although the operations on time series and spatial data may be different, there is some commonality in the sense of the data having ordered dimensions and the implementations may overlap.



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