Pack: comad comet demo workflow

Description:

The workflow analyzes meteorological data coming from comet project (http://comet.ucdavis.edu/wiki/index.php/Main_Page). The main analysis steps are:

1. Collect hourly humidity data in ten days from one weather station

2. Aggregate the hourly data based on a group of time window and calculate basic statistic data, like min, max etc, for the data aggregated in each window.

3. Calculate statistics of growing degree day for data in each window

4. Draw time-based trend graph for analysis and comparison

Description:

Comet workflow analyzes meteorological data coming from comet project (http://comet.ucdavis.edu/wiki/index.php/Main_Page). The main analysis steps are:
1.Collect hourly humidity data in ten days from one weather station
2.Aggregate the hourly data based on a group of time window and calculate basic statistic data, like min, max etc, for the data aggregated in each window.
3.Calculate statistics of growing degree day for data in each window
4.Draw time-based trend graph for analysis and comparison by using the Kepler SequencePlotter actor to show the comad sequence mode

Description:

Comet workflow analyzes meteorological data coming from comet project (http://comet.ucdavis.edu/wiki/index.php/Main_Page). The main analysis steps are:
1.Collect hourly humidity data in ten days from three weather stations
2.Aggregate the hourly data based on a group of time window and calculate basic statistic data, like min, max etc, for the data aggregated in each window.
3.Calculate statistics of growing degree day for data in each window
4.Draw time-based trend graph for analysis and comparison by using the Kepler RExpression actor

This workflow is compared with one_station_RPlotter and two_station_RPlotter to show the adaptability of the comad actor to the workflow with different data structure.

Description:

Comet workflow analyzes meteorological data coming from comet project (http://comet.ucdavis.edu/wiki/index.php/Main_Page). The main analysis steps are:
1.Collect hourly humidity data in ten days from two weather stations
2.Aggregate the hourly data based on a group of time window and calculate basic statistic data, like min, max etc, for the data aggregated in each window.
3.Calculate statistics of growing degree day for data in each window
4.Draw time-based trend graph for analysis and comparison by using the Kepler SequencePlotter actor to show the comad sequence mode

This workflow is compared with one_station_RPlotter and three_station_RPlotter to show the adaptability of the comad actor to the workflow with different data structure.