A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Approximate String Joins in a Database (Almost) for Free
Proceedings of the 27th International Conference on Very Large Data Bases
Robust and efficient fuzzy match for online data cleaning
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Efficient set joins on similarity predicates
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Scaling up all pairs similarity search
Proceedings of the 16th international conference on World Wide Web
Extending q-grams to estimate selectivity of string matching with low edit distance
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
ACM Transactions on Database Systems (TODS)
Efficient similarity joins for near duplicate detection
Proceedings of the 17th international conference on World Wide Web
An efficient filter for approximate membership checking
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Hashed samples: selectivity estimators for set similarity selection queries
Proceedings of the VLDB Endowment
Ed-Join: an efficient algorithm for similarity joins with edit distance constraints
Proceedings of the VLDB Endowment
Scalable ad-hoc entity extraction from text collections
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
Fast Indexes and Algorithms for Set Similarity Selection Queries
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
Incremental maintenance of length normalized indexes for approximate string matching
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Efficient approximate entity extraction with edit distance constraints
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Power-law based estimation of set similarity join size
Proceedings of the VLDB Endowment
Efficient parallel set-similarity joins using MapReduce
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Bed-tree: an all-purpose index structure for string similarity search based on edit distance
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Trie-join: efficient trie-based string similarity joins with edit-distance constraints
Proceedings of the VLDB Endowment
Faerie: efficient filtering algorithms for approximate dictionary-based entity extraction
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Fast-join: An efficient method for fuzzy token matching based string similarity join
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Can we beat the prefix filtering?: an adaptive framework for similarity join and search
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Seal: spatio-textual similarity search
Proceedings of the VLDB Endowment
Star-Join: spatio-textual similarity join
Proceedings of the 21st ACM international conference on Information and knowledge management
Landmark-join: hash-join based string similarity joins with edit distance constraints
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Efficient edit distance based string similarity search using deletion neighborhoods
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Approximate string matching by position restricted alignment
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Cache-aware parallel approximate matching and join algorithms using BWT
Proceedings of the Joint EDBT/ICDT 2013 Workshops
String similarity measures and joins with synonyms
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A partition-based method for string similarity joins with edit-distance constraints
ACM Transactions on Database Systems (TODS)
Asymmetric signature schemes for efficient exact edit similarity query processing
ACM Transactions on Database Systems (TODS)
A human-machine method for web table understanding
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Extending string similarity join to tolerant fuzzy token matching
ACM Transactions on Database Systems (TODS)
Scalable column concept determination for web tables using large knowledge bases
Proceedings of the VLDB Endowment
Efficient error-tolerant query autocompletion
Proceedings of the VLDB Endowment
Efficient processing of graph similarity queries with edit distance constraints
The VLDB Journal — The International Journal on Very Large Data Bases
Hi-index | 0.00 |
As an essential operation in data cleaning, the similarity join has attracted considerable attention from the database community. In this paper, we study string similarity joins with edit-distance constraints, which find similar string pairs from two large sets of strings whose edit distance is within a given threshold. Existing algorithms are efficient either for short strings or for long strings, and there is no algorithm that can efficiently and adaptively support both short strings and long strings. To address this problem, we propose a partition-based method called Pass-Join. Pass-Join partitions a string into a set of segments and creates inverted indices for the segments. Then for each string, Pass-Join selects some of its substrings and uses the selected substrings to find candidate pairs using the inverted indices. We devise efficient techniques to select the substrings and prove that our method can minimize the number of selected substrings. We develop novel pruning techniques to efficiently verify the candidate pairs. Experimental results show that our algorithms are efficient for both short strings and long strings, and outperform state-of-the-art methods on real datasets.