Fuzzy queries in multimedia database systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Interactive deduplication using active learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Blocking: Learning to Scale Up Record Linkage
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Example-driven design of efficient record matching queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient online top-K retrieval with arbitrary similarity measures
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Learning blocking schemes for record linkage
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Similarity-aware indexing for real-time entity resolution
Proceedings of the 18th ACM conference on Information and knowledge management
An Introduction to Duplicate Detection
An Introduction to Duplicate Detection
Similarity Search: The Metric Space Approach
Similarity Search: The Metric Space Approach
Efficient similarity search: arbitrary similarity measures, arbitrary composition
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Similarity search aims to find all objects similar to a query object. Typically, some base similarity measures for the different properties of the objects are defined, and light-weight similarity indexes for these measures are built. A query plan specifies which similarity indexes to use with which similarity thresholds and how to combine the results. Previous work creates only a single, static query plan to be used by all queries. In contrast, our approach creates a new plan for each query. We introduce the novel problem of query planning for similarity search, i.e., selecting for each query the plan that maximizes completeness of the results with cost below a query-specific limit. By regarding the frequencies of attribute values we are able to better estimate plan completeness and cost, and thus to better distribute our similarity comparisons. Evaluation on a large real-world dataset shows that our approach significantly reduces cost variance and increases overall result completeness compared to static query plans.