Abstract
A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolutioncan be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR1) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data informsover ↔ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the ACM SIGMOD International Conference on Management of Data |
| Pages | 151-162 |
| Number of pages | 12 |
| DOIs | |
| State | Published - 2007 |
| Event | SIGMOD 2007: ACM SIGMOD International Conference on Management of Data - Beijing, China Duration: Jun 12 2007 → Jun 14 2007 |
Other
| Other | SIGMOD 2007: ACM SIGMOD International Conference on Management of Data |
|---|---|
| Country | China |
| City | Beijing |
| Period | 6/12/07 → 6/14/07 |
Fingerprint
Keywords
- Conflicts
- Query processing
- Reasoning with misaligned data
- Relevance feedback
- Taxonomy
ASJC Scopus subject areas
- Computer Science(all)
Cite this
FICSR : Feedback-based inconsistency resolution and query processing on misaligned data sources. / Qi, Yan; Candan, Kasim; Sapino, Maria Luisa.
Proceedings of the ACM SIGMOD International Conference on Management of Data. 2007. p. 151-162.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - FICSR
T2 - Feedback-based inconsistency resolution and query processing on misaligned data sources
AU - Qi, Yan
AU - Candan, Kasim
AU - Sapino, Maria Luisa
PY - 2007
Y1 - 2007
N2 - A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolutioncan be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR1) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data informsover ↔ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.
AB - A critical reality in data integration is that knowledge from different sources may often be conflicting with each other. Conflict resolutioncan be costly and, if done without proper context, can be ineffective. In this paper, we propose a novel query-driven and feedback-based approach (FICSR1) to conflict resolution when integrating data sources. In particular, instead of relying on traditional model based definition of consistency, we introduce a ranked interpretation. This not only enables FICSR to deal with the complexity of the conflict resolution process, but also helps achieve a more direct match between the users' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for bi-directional (data informsover ↔ user) feedback cycle for conflict resolution: given a query, (a) a preliminary ranking of candidate results on data can inform the user regarding constraints critical to the query, while (b) user feedback regarding the ranks can be exploited to inform the system about user's relevant domain knowledge. To enable this feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated data as well as for on-line query processing, candidate result enumeration, and validity analysis. The results are brought together and evaluated in the FICSR system.
KW - Conflicts
KW - Query processing
KW - Reasoning with misaligned data
KW - Relevance feedback
KW - Taxonomy
UR - http://www.scopus.com/inward/record.url?scp=35448983201&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35448983201&partnerID=8YFLogxK
U2 - 10.1145/1247480.1247499
DO - 10.1145/1247480.1247499
M3 - Conference contribution
AN - SCOPUS:35448983201
SN - 1595936866
SN - 9781595936868
SP - 151
EP - 162
BT - Proceedings of the ACM SIGMOD International Conference on Management of Data
ER -