Data models, database languages and database management systems
Data models, database languages and database management systems
The merge/purge problem for large databases
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Meaningful change detection in structured data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Consistent query answers in inconsistent databases
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Discovering and reconciling value conflicts for numerical data integration
Information Systems - Data extraction, cleaning and reconciliation
Integration of Time Versions into a Relational Database System
VLDB '84 Proceedings of the 10th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Snapshot Differential Algorithms for Data Warehousing
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FARMER: finding interesting rule groups in microarray datasets
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining for patterns in contradictory data
Proceedings of the 2004 international workshop on Information quality in information systems
A cost-based model and effective heuristic for repairing constraints by value modification
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Improving data quality by source analysis
Journal of Data and Information Quality (JDIQ)
FoIKS'10 Proceedings of the 6th international conference on Foundations of Information and Knowledge Systems
Annals of Mathematics and Artificial Intelligence
| Hi-index | 0.00 |
We study the novel problem of efficiently computing the update distance for a pair of relational databases. In analogy to the edit distance of strings, we define the update distance of two databases as the minimal number of set-oriented insert, delete and modification operations necessary to transform one database into the other. We show how this distance can be computed by traversing a search space of database instances connected by update operations. This insight leads to a family of algorithms that compute the update distance or approximations of it. In our experiments we observed that a simple heuristic performs surprisingly well in most considered cases.Our motivation for studying distance measures for databases stems from the field of scientific databases. There, replicas of a single database are often maintained at different sites, which typically leads to (accidental or planned) divergence of their content. To re-create a consistent view, these differences must be resolved. Such an effort requires an understanding of the process that produced them. We found that minimal update sequences of set-oriented update operations are a proper and concise representation of systematic errors, thus giving valuable clues to domain experts responsible for conflict resolution.