Why decision support fails and how to fix it
ACM SIGMOD Record
“One size fits all” database architectures do not work for DSS
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Database performance in the real world: TPC-D and SAP R/3
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Volcano— An Extensible and Parallel Query Evaluation System
IEEE Transactions on Knowledge and Data Engineering
Teaching an OLTP Database Kernel Advanced Data Warehousing Techniques
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
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
STORM: A Statistical Object Representation Model
Proceedings of the 5th International Conference SSDBM on Statistical and Scientific Database Management
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Spreadsheets in RDBMS for OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Buffering databse operations for enhanced instruction cache performance
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Extending XQuery for analytics
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Sybase IQ multiplex - designed for analytics
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Towards a SOA infrastructure for statistically analysing public health data
Proceedings of the ACM first workshop on CyberInfrastructure: information management in eScience
A SOA statistical engine for biomedical data
Computer Methods and Programs in Biomedicine
Dwarfs in the rearview mirror: how big are they really?
Proceedings of the VLDB Endowment
IEICE - Transactions on Information and Systems
Preference-Based Recommendations for OLAP Analysis
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
The NOX framework: native language queries for business intelligence applications
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
θ-Constrained multi-dimensional aggregation
Information Systems
Detecting summarizability in OLAP
Data & Knowledge Engineering
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In the last ten years, database vendors have invested heavily in order to extend their products with new features for decision support. Examples of functionality that has been added are top N [2], ranking [13, 7], spreadsheet computations [19], grouping sets [14], data cube [9], and moving sums [15] in order to name just a few. Unfortunately, many modern OLAP systems do not use that functionality or replicate a great deal of it in addition to other database-related functionality. In fact, the gap between the functionality provided by an OLAP system and the functionality used from the underlying database systems has widened in the past, rather than narrowed. The reasons for this trend are that SQL as a data definition and query language, the relational model, and the client/server architecture of the current generation of database products have fundamental shortcomings for OLAP. This paper lists these deficiencies and presents the BTell OLAP engine as an example on how to bridge these shortcomings. In addition, we discuss how to extend current DBMS to better support OLAP in the future.