Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Approximate String Joins in a Database (Almost) for Free
Proceedings of the 27th International Conference on Very Large Data Bases
Learning domain-independent string transformation weights for high accuracy object identification
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text joins in an RDBMS for web data integration
WWW '03 Proceedings of the 12th international conference on World Wide Web
Temporal Data and the Relational Model
Temporal Data and the Relational Model
Efficient set joins on similarity predicates
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Integrating XML and Relational Database Systems
World Wide Web
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
LinkClus: efficient clustering via heterogeneous semantic links
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Benchmarking declarative approximate selection predicates
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Eliminating fuzzy duplicates in data warehouses
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Merging the results of approximate match operations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Example-driven design of efficient record matching queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Efficient similarity joins for near duplicate detection
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Ed-Join: an efficient algorithm for similarity joins with edit distance constraints
Proceedings of the VLDB Endowment
Efficient Merging and Filtering Algorithms for Approximate String Searches
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Space-Constrained Gram-Based Indexing for Efficient Approximate String Search
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient top-k algorithms for fuzzy search in string collections
Proceedings of the First International Workshop on Keyword Search on Structured Data
Efficient approximate entity extraction with edit distance constraints
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A Wikipedia Matching Approach to Contextual Advertising
World Wide Web
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We study the problem of efficiently extracting K entities, in a temporal database, which are most similar to a given search query. This problem is well studied in relational databases, where each entity is represented as a single record and there exist a variety of methods to define the similarity between a record and the search query. However, in temporal databases, each entity is represented as a sequence of historical records. How to properly define the similarity of each entity in the temporal database still remains an open problem. The main challenging is that, when a user issues a search query for an entity, he or she is prone to mix up information of the same entity at different time points. As a result, methods, which are used in relational databases based on record granularity, cannot work any further. Instead, we regard each entity as a set of "virtual records", where attribute values of a "virtual record" can be from different records of the same entity. In this paper, we propose a novel evaluation model, based on which the similarity between each "virtual record" and the query can be effectively quantified, and the maximum similarity of its "virtual records" is taken as the similarity of an entity. For each entity, as the number of its "virtual records" is exponentially large, calculating the similarity of the entity is challenging. As a result, we further propose a Dominating Tree Algorithm (DTA), which is based on the bounding-pruning-refining strategy, to efficiently extract K entities with greatest similarities. We conduct extensive experiments on both real and synthetic datasets. The encouraging results show that our model for defining the similarity between each entity and the search query is effective, and the proposed DTA can perform at least two orders of magnitude improvement on the performance comparing with the naive approach.