Exploiting similarity-aware grouping in decision support systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
SimDB: a similarity-aware database system
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On a fuzzy group-by and its use for fuzzy association rule mining
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Spatial queries with two kNN predicates
Proceedings of the VLDB Endowment
Aggregating and disaggregating flexibility objects
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Pattern discovery in data streams under the time warping distance
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity queries: their conceptual evaluation, transformations, and processing
The VLDB Journal — The International Journal on Very Large Data Bases
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Group-by is a core database operation that is used extensively in OLTP, OLAP, and decision support systems. In many application scenarios, it is required to group similar but not necessarily equal values. In this paper we propose a new SQL construct that supports similarity-based Group-by (SGB). SGB is not a new clustering algorithm, but rather is a practical and fast similarity grouping query operator that is compatible with other SQL operators and can be combined with them to answer similarity-based queries efficiently. In contrast to expensive clustering algorithms, the proposed similarity group-by operator maintains low execution times while still generating meaningful groupings that address many application needs. The paper presents a general definition of the similarity group-by operation and gives three instances of this definition. The paper also discusses how optimization techniques for the regular group-by can be extended to the case of SGB. The proposed operators are implemented inside PostgreSQL. The performance study shows that the proposed similarity-based group-by operators have good scalability properties with at most only 25% increase in execution time over the regular group-by.