View source for Spatial and temporal reconciliation to support dataset comparisons

==Use Case AQ.Comparisons.1.a==
Spatial and temporal reconciliation to support dataset comparisons

==Purpose==
To process datasets so that they align in spatial and temporal dimensions, thereby facilitating data comparisons. For example, if 

==Revision Information==
Version 0.1.a 

Prepared by: 
Stefan Falke<br>
Washington University and
Northrop Grumman IT - TASC

created: May 11, 2007

Revision History

Modified by 
<Modifier Name/Affil>, 
<Date/time>, 
<Brief Description>

==Use Case Identification==

===Use Case Designation===

AQ.Comparisons.1.a

===Use Case Name===
Short name: Dataset comparisons

Long name: Reconciliation of datasets for spatial and temporal comparisons

==Use Case Definition==
The combination or integration of independent data sources often generates new insight into the characterization and analysis of air pollution. Integrating datasets is often difficult because they are collected or generated a different spatial and temporal resolutions. For example, in comparing a point dataset (surface monitor data) and a gridded model output, either the point data must be interpolated to a grid or the gridded model values must be extracted at the corresponding points. As web information systems advance beyond data access and visualization of individual datasets, there is a need to develop web services to process datasets to a common spatial and temporal framework. Aligning datasets to a common spatial-temporal framework (common spatial resolutions or representative times) is needed to support comparisons among the datasets.

===Actors===

====Primary Actors====
Air quality analyst who seeks to either directly compare two datasets or who needs to modify a dataset so that it is suitable as input into a data analysis exercise. 

====Other Actors====
 
===Preconditions===
*1. Datasets are accessible through standard interfaces
*2. Dataset format types are known
*3. A spatial-temporal framework for the analysis is provided by user.

===Postconditions===
*1. 
*2. 
*3. 

===Normal Flow (Process Model)===
*1) Define spatial-temporal framework for comparative analysis (e.g., what's the ratio between these two SO2 emissions model outputs over the contiguous U.S. for May 1, 2005?)
*2) Data access (independent process for each dataset being compared)
*3) Data processing to interpolate/extrapolate to a common space-time framework
*4) Data comparison

===Alternative Flows===

===Successful Outcomes===
*1. Meaningful data comparisons.

===Failure Outcomes===
*1. Misalignment error in spatial-temporal framework among datasets
*2.

===Special Functional Requirements===
None

===Extension Points===
*<Cluster>.<SubArea>.<number>.<letter 1> 

==Diagrams==

===Use Case Diagram===


===State Diagram (optional)===

===Activity Diagram (optional)===

===Other Diagrams (optional)===

==Non-Functional Requirements (optional)==

===Performance===

===Reliability===

===Scalability===

===Usability===

===Security===

===Other Non-functional Requirements===

==Selected Approach==

===Overall Technical Approach===

===Architecture===

===Participating Organizations/Projects===

===Technology A===

====Description====

====Benefits====

====Limitations====

===Technology B===

====Description====

====Benefits====

====Limitations====

==References (optional)==