SchemaSQL: An extension to SQL for multidatabase interoperability
ACM Transactions on Database Systems (TODS)
Database Management Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
SchemaSQL - A Language for Interoperability in Relational Multi-Database Systems
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Relational languages for metadata integration
ACM Transactions on Database Systems (TODS)
PIVOT and UNPIVOT: optimization and execution strategies in an RDBMS
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Data exchange with data-metadata translations
Proceedings of the VLDB Endowment
Definition and Formalization of Entity Resolution Functions for Everyday Information Integration
Semantics in Data and Knowledge Bases
Updatable and evolvable transforms for virtual databases
Proceedings of the VLDB Endowment
Efficient resource attribute retrieval in RDF triple stores
Proceedings of the 20th ACM international conference on Information and knowledge management
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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PIVOT is an important relational operation that allows data in rows to be exchanged for columns. Although most current relational database management systems support PIVOT-type operations, to date a purely formal, algebraic characterization of PIVOT has been lacking. In this paper, we present a characterization in terms of extended relational algebra operators τ (transpose), Π (drop projection), and μ (unique optimal tuple merge). This enables us to (1) draw parallels with PIVOT and existing operators employed in Dynamic Data Mapping Systems (DDMS), (2) formally characterize invertible PIVOT instances, and (3) provide complexity results for PIVOT-type operations. These contributions are an important part of ongoing work on formal models for relational OLAP.