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
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, 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
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>
