Excel 2000 in a Nutshell: A Power User's Quick Reference
Excel 2000 in a Nutshell: A Power User's Quick Reference
Microsoft Excel 2000 Functions in Practice
Microsoft Excel 2000 Functions in Practice
Microsoft Olap Unleashed
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Advanced SQL modeling in RDBMS
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
BusinessObjects XI (Release 2): The Complete Reference
BusinessObjects XI (Release 2): The Complete Reference
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
Query-through-drilldown: data-oriented extensional queries
AVI '08 Proceedings of the working conference on Advanced visual interfaces
The Tradeoff of Delta Table Merging and Re-writing Algorithms in What-If Analysis Application
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
An efficient sheet partition technique for very large relational tables in OLAP
BNCOD'07 Proceedings of the 24th British national conference on Databases
Spreadsheet as a relational database engine
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
θ-Constrained multi-dimensional aggregation
Information Systems
Supporting real-time supply chain decisions based on RFID data streams
Journal of Systems and Software
Automatic web spreadsheet data extraction
Proceedings of the 3rd International Workshop on Semantic Search Over the Web
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Spreadsheets, and MS Excel in particular, are established analysis tools. They offer an attractive user interface, provide an easy to use computational model, and offer substantial interactivity for what-if analysis. However, as opposed to RDBMS, spreadsheets do not provide a central repository hence they do not provide shareability of models built in Excel and lead to proliferation of multiple copies of the same spreadsheet. Furthermore, spreadsheets do not offer scalable computation, for example, they lack parallelization. To address the shareability, and scalability problems, we propose to automatically translate Excel computation into SQL. An analyst can import the data from a relational system, define computation over it using familiar Excel formulas and then translate and store it as a relational SQL view over the imported data. The Excel computation is then performed by the relational system. To edit the model, the analyst can bring the model back to Excel, modify it in Excel and store it back as an SQL View. We refer to this system as Query by Excel, QBX in short.