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

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4727?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-4727.
------------------------------
    Resolution: Won't Fix

I suggest the timeseries ideas be implemented in the spark-timeseries project since that's already running with the idea. It doesn't cover spatial. But both seem like app-space concerns for a separate library rather than Spark core.

> 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.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org