- Differentiate among modeling uncertainty for entire datasets, for features, and for individual data values
- Describe SQL extensions for querying uncertainty information in databases
- Describe extensions to relational DBMS to represent different types of uncertainty in attributes, including both vagueness/fuzziness and error-based uncertainty
- Discuss the role of metadata in representing and communicating dataset-level uncertainty
- Create a GIS database that models uncertain information
- Identify whether it is important to represent uncertainty in a particular GIS application
- Describe the architecture of data models (both field- and object-based) to represent feature-level and datum-level uncertainty
- Evaluate the advantages and disadvantages of existing uncertainty models based on storage efficiency, query performance, ease of data entry, and ability to implement in existing software
Data management involves the theories and techniques for managing the entire data lifecycle, from data collection to data format conversion, from data storage to data sharing and retrieval, to data provenance, data quality control and data curation for long-term data archival and preservation.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.