Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
View maintenance in a warehousing environment
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
NonStop SQL/MX primitives for knowledge discovery
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Answering complex SQL queries using automatic summary tables
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Horizontal aggregations for building tabular data sets
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Vector and matrix operations programmed with UDFs in a relational DBMS
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Efficient computation of PCA with SVD in SQL
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Can we analyze big data inside a DBMS?
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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Existing SQL aggregate functions present important limitations to compute percentages. This article proposes two SQL aggregate functions to compute percentages addressing such limitations. The first function returns one row for each percentage in vertical form like standard SQL aggregations. The second function returns each set of percentages adding 100% on the same row in horizontal form. These novel aggregate functions are used as a framework to introduce the concept of percentage queries and to generate efficient SQL code. Experiments study different percentage query optimization strategies and compare evaluation time of percentage queries taking advantage of our proposed aggregations against queries using available OLAP extensions. The proposed percentage aggregations are easy to use, have wide applicability and can be efficiently evaluated.