Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage
Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where mashups facilitate the combination of data from different sources....
| Main Authors: | , |
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| 格式: | Working Paper |
| 语言: | en_US |
| 出版: |
2008
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| 主题: | |
| 在线阅读: | http://hdl.handle.net/1721.1/40085 |
| _version_ | 1826213753515933696 |
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| author | Prat, Nicolas Madnick, Stuart E. |
| author_facet | Prat, Nicolas Madnick, Stuart E. |
| author_sort | Prat, Nicolas |
| collection | MIT |
| description | Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability,
a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where
mashups facilitate the combination of data from different sources. Our approach for assessing data believability is
based on provenance and lineage, i.e. the origin and subsequent processing history of data. We present the main
concepts of our model for representing and storing data provenance, and an ontology of the sub-dimensions of data
believability. We then use aggregation operators to compute believability across the sub-dimensions of data
believability and the provenance of data. We illustrate our approach with a scenario based on Internet data. Our
contribution lies in three main design artifacts (1) the provenance model (2) the ontology of believability subdimensions
and (3) the method for computing and aggregating data believability. To our knowledge, this is the first
work to operationalize provenance-based assessment of data believability. |
| first_indexed | 2024-09-23T15:54:16Z |
| format | Working Paper |
| id | mit-1721.1/40085 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:54:16Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | mit-1721.1/400852019-04-12T09:31:47Z Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage Prat, Nicolas Madnick, Stuart E. Data Lineage Web 2.0 Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where mashups facilitate the combination of data from different sources. Our approach for assessing data believability is based on provenance and lineage, i.e. the origin and subsequent processing history of data. We present the main concepts of our model for representing and storing data provenance, and an ontology of the sub-dimensions of data believability. We then use aggregation operators to compute believability across the sub-dimensions of data believability and the provenance of data. We illustrate our approach with a scenario based on Internet data. Our contribution lies in three main design artifacts (1) the provenance model (2) the ontology of believability subdimensions and (3) the method for computing and aggregating data believability. To our knowledge, this is the first work to operationalize provenance-based assessment of data believability. 2008-01-11T18:15:00Z 2008-01-11T18:15:00Z 2008-01-11T18:15:00Z Working Paper http://hdl.handle.net/1721.1/40085 en_US MIT Sloan School of Management Working Paper 4670-07 application/pdf |
| spellingShingle | Data Lineage Web 2.0 Prat, Nicolas Madnick, Stuart E. Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title | Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title_full | Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title_fullStr | Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title_full_unstemmed | Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title_short | Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage |
| title_sort | evaluating and aggregating data believability across quality sub dimensions and data lineage |
| topic | Data Lineage Web 2.0 |
| url | http://hdl.handle.net/1721.1/40085 |
| work_keys_str_mv | AT pratnicolas evaluatingandaggregatingdatabelievabilityacrossqualitysubdimensionsanddatalineage AT madnickstuarte evaluatingandaggregatingdatabelievabilityacrossqualitysubdimensionsanddatalineage |