An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Data warehouse design from XML sources
Proceedings of the 4th ACM international workshop on Data warehousing and OLAP
Specifying OLAP Cubes on XML Data
Journal of Intelligent Information Systems
Multidimensional Modeling with UML Package Diagrams
ER '02 Proceedings of the 21st International Conference on Conceptual Modeling
A Temporal Query Language for OLAP: Implementation and a Case Study
DBPL '01 Revised Papers from the 8th International Workshop on Database Programming Languages
Towards a spatial multidimensional model
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
Integration and dimensional modeling approaches for complex data warehousing
Journal of Global Optimization
A method for online analytical processing of text data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Data & Knowledge Engineering
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Nowadays, multidimensional models are recognized to best reflect the decision makers' analytical view of data. The classical multi-dimensional models were meant to analyze conventional data (numerical and categorical). However, they fail to handle data complexity, which is expressed by the multiplicity of data sources, the heterogeneity of formats, the diversity of structures, etc. To this end, new multidimensional models have been proposed for OLAP purposes. Nevertheless, data complexity is partially covered in these models, which may cause a lack in decision making. In our previous work, we proposed to integrate data complexity within a complex object-based multidimensional model. In this paper, based on our proposed model, we provide adapted OLAP operators that take into account data complexity. Thus, we define operators to create complex data cubes, to visualize them and to analyze them.