A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
Reasoning about Summarizability in Heterogeneous Multidimensional Schemas
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Extending Practical Pre-Aggregation in On-Line Analytical Processing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Datawarehousing Has More Colours Than Just Black & White
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
The TreeScape System: Reuse of Pre-Computed Aggregates over Irregular OLAP Hierarchies
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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
Extending the E/R Model for the Multidimensional Paradigm
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
From analysis to interactive exploration: building visual hierarchies from OLAP cubes
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Exploring OLAP aggregates with hierarchical visualization techniques
Proceedings of the 2007 ACM symposium on Applied computing
Dimensional hierarchies: implementation in data warehouse logical scheme design
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
H-IQTS: a semantics-aware histogram for compressing categorical OLAP data
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Solving summarizability problems in fact-dimension relationships for multidimensional models
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Proceedings of the International Conference on Advances in Computing, Communication and Control
Model-Driven Development for Enabling the Feadback from Warehouses and OLAP to Operational Systems
Proceedings of the 2007 conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS'2006
Enabling OLAP in mobile environments via intelligent data cube compression techniques
Journal of Intelligent Information Systems
A survey on summarizability issues in multidimensional modeling
Data & Knowledge Engineering
Hetero-homogeneous hierarchies in data warehouses
APCCM '10 Proceedings of the Seventh Asia-Pacific Conference on Conceptual Modelling - Volume 110
Complexity study on "Carry-along Sort" algorithm
ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
OLAP technology for business process intelligence: challenges and solutions
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on meta-data which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.